From 75589ba321ebb0d1552e6c815d9cda1ad083b23d Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Mon, 3 Feb 2025 16:25:08 -0800 Subject: [PATCH 01/13] add sample parameter and catalogue files used for E3SM zppy workflow --- .../E3SM/enso/e3sm_enso_parameter_file.py | 94 ++ .../E3SM/enso/obs_catalogue.json | 186 +++ .../E3SM/enso/obs_landmask.json | 3 + .../climo_mean_climate_catalogue.json | 461 ++++++++ .../climo_ref_mean_climate_catalogue.json | 461 ++++++++ .../e3sm_mean_climate_parameter_file.py | 152 +++ .../E3SM/mean_climate/reference_alias.json | 341 ++++++ .../E3SM/mean_climate/regions_specs.json | 263 +++++ .../mean_climate/synthetic_metrics_list.json | 38 + .../E3SM/util/link_observation.py | 103 ++ .../E3SM/util/pcmdi_zppy_util.py | 1019 +++++++++++++++++ .../e3sm_mov_atm_parameter_file.py | 131 +++ .../e3sm_mov_cpl_parameter_file.py | 130 +++ ...s_ref_variability_modes_atm_catalogue.json | 19 + ...s_ref_variability_modes_cpl_catalogue.json | 19 + .../ts_variability_modes_atm_catalogue.json | 19 + .../ts_variability_modes_cpl_catalogue.json | 19 + 17 files changed, 3458 insertions(+) create mode 100755 sample_setups/external-setups/E3SM/enso/e3sm_enso_parameter_file.py create mode 100755 sample_setups/external-setups/E3SM/enso/obs_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/enso/obs_landmask.json create mode 100755 sample_setups/external-setups/E3SM/mean_climate/climo_mean_climate_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/mean_climate/climo_ref_mean_climate_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/mean_climate/e3sm_mean_climate_parameter_file.py create mode 100755 sample_setups/external-setups/E3SM/mean_climate/reference_alias.json create mode 100755 sample_setups/external-setups/E3SM/mean_climate/regions_specs.json create mode 100755 sample_setups/external-setups/E3SM/mean_climate/synthetic_metrics_list.json create mode 100755 sample_setups/external-setups/E3SM/util/link_observation.py create mode 100755 sample_setups/external-setups/E3SM/util/pcmdi_zppy_util.py create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_atm_parameter_file.py create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_cpl_parameter_file.py create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_atm_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_cpl_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_atm_catalogue.json create mode 100755 sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_cpl_catalogue.json diff --git a/sample_setups/external-setups/E3SM/enso/e3sm_enso_parameter_file.py b/sample_setups/external-setups/E3SM/enso/e3sm_enso_parameter_file.py new file mode 100755 index 000000000..1775c4c4f --- /dev/null +++ b/sample_setups/external-setups/E3SM/enso/e3sm_enso_parameter_file.py @@ -0,0 +1,94 @@ +import os +import sys +import json + +##################### +#basic information +##################### +model_name = 'e3sm.historical.v3-LR.0051' +start_yr = int('1980') +end_yr = int('2015') +num_years = end_yr - start_yr + 1 +period = "{:04d}{:02d}-{:04d}{:02d}".format(start_yr,1,end_yr,12) + +mip = model_name.split(".")[0] +exp = model_name.split(".")[1] +product = model_name.split(".")[2] +realm = model_name.split(".")[3] + +############################################## +#Configuration shared with pcmdi diagnostics +############################################## +# Record NetCDF output +nc_out_obs = True +nc_out_model = True +if nc_out_model or nc_out_obs: + ext = ".nc" +else: + ext = ".xml" +user_notes = 'Provenance and results' +debug = False + +# Generate plots +plot_model = True +plot_obs = True # optional + +# Additional settings +run_type = 'model_vs_obs' +figure_format = 'png' + +# Save interpolated model climatology ? +save_test_clims = True + +# Save Metrics Results in Single File ? +# option: 'y' or 'n', set to 'n' as we +# run pcmdi for each variable separately +metrics_in_single_file = 'n' + +# customize land/sea mask values +regions_values = {"land":100.,"ocean":0.} + +#setup template for land/sea mask (fixed) +modpath_lf = os.path.join( + 'fixed', + 'sftlf.%(model).nc' +) + +############################################ +#setup specific for mean climate metrics +########################################### +#parameter setup specific for enso metrics +########################################### +modnames = [ product ] +realization = realm +modpath = os.path.join( + 'ts', + '{}.{}.%(model).%(realization).{}.%(variable).{}.nc'.format(mip,exp,'Amon',period) +) + +#observation/reference file catalogue +obs_cmor = True +obs_cmor_path = './' +obs_catalogue = 'obs_catalogue.json' + +#land/sea mask for obs/reference model +reference_data_lf_path = json.load(open('obs_landmask.json')) + +# METRICS COLLECTION (set in namelist, and main driver) +# metricsCollection = ENSO_perf # ENSO_perf, ENSO_tel, ENSO_proc + +# OUTPUT +results_dir = os.path.join( + 'pcmdi_diags', + '%(output_type)', + 'enso_metric', + '%(mip)', + '%(exp)', + 'v20250131', + '%(metricsCollection)', +) + +json_name = "%(mip)_%(exp)_%(metricsCollection)_v20250131_%(model)_%(realization)" + +netcdf_name = json_name + diff --git a/sample_setups/external-setups/E3SM/enso/obs_catalogue.json b/sample_setups/external-setups/E3SM/enso/obs_catalogue.json new file mode 100755 index 000000000..00de02307 --- /dev/null +++ b/sample_setups/external-setups/E3SM/enso/obs_catalogue.json @@ -0,0 +1,186 @@ +{ + "ERA5": { + "psl": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "psl", + "var_name": "psl", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.psl.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.psl.198001-201512.nc" + }, + "pr": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "pr", + "var_name": "pr", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.pr.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.pr.198001-201512.nc" + }, + "prsn": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "prsn", + "var_name": "prsn", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.prsn.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.prsn.198001-201512.nc" + }, + "ts": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "ts", + "var_name": "ts", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.ts.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.ts.198001-201512.nc" + }, + "tas": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "tas", + "var_name": "tas", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.tas.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.tas.198001-201512.nc" + }, + "tauu": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "tauu", + "var_name": "tauu", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.tauu.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.tauu.198001-201512.nc" + }, + "tauv": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "tauv", + "var_name": "tauv", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.tauv.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.tauv.198001-201512.nc" + }, + "hfls": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "hfls", + "var_name": "hfls", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.hfls.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.hfls.198001-201512.nc" + }, + "hfss": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "hfss", + "var_name": "hfss", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.hfss.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.hfss.198001-201512.nc" + }, + "rlds": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "rlds", + "var_name": "rlds", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.rlds.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.rlds.198001-201512.nc" + }, + "rsds": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "rsds", + "var_name": "rsds", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.rsds.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.rsds.198001-201512.nc" + }, + "rlus": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "rlus", + "var_name": "rlus", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.rlus.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.rlus.198001-201512.nc" + }, + "rlut": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "rlut", + "var_name": "rlut", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.rlut.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.rlut.198001-201512.nc" + }, + "rsdt": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198001", + "yymme": "201512", + "var_in_file": "rsdt", + "var_name": "rsdt", + "file_path": "ts_ref/obs.historical.ERA5.00.Amon.rsdt.198001-201512.nc", + "template": "obs.historical.ERA5.00.Amon.rsdt.198001-201512.nc" + } + } +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/enso/obs_landmask.json b/sample_setups/external-setups/E3SM/enso/obs_landmask.json new file mode 100755 index 000000000..46151e7c3 --- /dev/null +++ b/sample_setups/external-setups/E3SM/enso/obs_landmask.json @@ -0,0 +1,3 @@ +{ + "ERA5": "fixed/sftlf.ERA5.nc" +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/mean_climate/climo_mean_climate_catalogue.json b/sample_setups/external-setups/E3SM/mean_climate/climo_mean_climate_catalogue.json new file mode 100755 index 000000000..ed062400c --- /dev/null +++ b/sample_setups/external-setups/E3SM/mean_climate/climo_mean_climate_catalogue.json @@ -0,0 +1,461 @@ +{ + "pr": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "pr", + "var_name": "pr", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.pr.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.pr.198501-201412.AC.v20250131.nc" + } + }, + "prw": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "prw", + "var_name": "prw", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.prw.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.prw.198501-201412.AC.v20250131.nc" + } + }, + "psl": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "psl", + "var_name": "psl", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.psl.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.psl.198501-201412.AC.v20250131.nc" + } + }, + "rlds": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rlds", + "var_name": "rlds", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rlds.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rlds.198501-201412.AC.v20250131.nc" + } + }, + "rldscs": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rldscs", + "var_name": "rldscs", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rldscs.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rldscs.198501-201412.AC.v20250131.nc" + } + }, + "rltcre": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rltcre", + "var_name": "rltcre", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rltcre.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rltcre.198501-201412.AC.v20250131.nc" + } + }, + "rstcre": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rstcre", + "var_name": "rstcre", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rstcre.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rstcre.198501-201412.AC.v20250131.nc" + } + }, + "rsus": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rsus", + "var_name": "rsus", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rsus.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rsus.198501-201412.AC.v20250131.nc" + } + }, + "rsuscs": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rsuscs", + "var_name": "rsuscs", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rsuscs.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rsuscs.198501-201412.AC.v20250131.nc" + } + }, + "rlus": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rlus", + "var_name": "rlus", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rlus.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rlus.198501-201412.AC.v20250131.nc" + } + }, + "rlut": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rlut", + "var_name": "rlut", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rlut.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rlut.198501-201412.AC.v20250131.nc" + } + }, + "rlutcs": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "rlutcs", + "var_name": "rlutcs", + "file_path": "climo/e3sm.historical.v3-LR.0051.Amon.rlutcs.198501-201412.AC.v20250131.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.rlutcs.198501-201412.AC.v20250131.nc" + } + }, + "rsds": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": 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+ "tauu": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "tauu", + "var_name": "tauu", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.tauu.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.tauu.198501-201412.AC.v20250131.nc" + } + }, + "tauv": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "tauv", + "var_name": "tauv", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.tauv.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.tauv.198501-201412.AC.v20250131.nc" + } + }, + "ts": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "ts", + "var_name": "ts", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.ts.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.ts.198501-201412.AC.v20250131.nc" + } + }, + "ta": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "ta", + "var_name": "ta-850", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.ta.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.ta.198501-201412.AC.v20250131.nc" + } + }, + "ua": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "ua", + "var_name": "ua-850", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.ua.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.ua.198501-201412.AC.v20250131.nc" + } + }, + "va": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "va", + "var_name": "va-850", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.va.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.va.198501-201412.AC.v20250131.nc" + } + }, + "zg": { + "set": "default", + "default": "ERA5", + "ERA5": { + "mip": "obs", + "exp": "historical", + "model": "ERA5", + "realization": "00", + "tableID": "Amon", + "yymms": "198501", + "yymme": "201412", + "var_in_file": "zg", + "var_name": "zg-500", + "file_path": "climo_ref/obs.historical.ERA5.00.Amon.zg.198501-201412.AC.v20250131.nc", + "template": "obs.historical.ERA5.00.Amon.zg.198501-201412.AC.v20250131.nc" + } + } +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/mean_climate/e3sm_mean_climate_parameter_file.py b/sample_setups/external-setups/E3SM/mean_climate/e3sm_mean_climate_parameter_file.py new file mode 100755 index 000000000..2e628e26d --- /dev/null +++ b/sample_setups/external-setups/E3SM/mean_climate/e3sm_mean_climate_parameter_file.py @@ -0,0 +1,152 @@ +import os +import sys +import json + +##################### +#basic information +##################### +model_name = "e3sm.historical.v3-LR.0051" +start_yr = int('1985') +end_yr = int('2014') +num_years = end_yr - start_yr + 1 +period = "{:04d}{:02d}-{:04d}{:02d}".format(start_yr,1,end_yr,12) + +mip = model_name.split(".")[0] +exp = model_name.split(".")[1] +product = model_name.split(".")[2] +realm = model_name.split(".")[3] +tableId = 'Amon' +case_id = 'v20250131' + +############################################## +#Configuration shared with pcmdi diagnostics +############################################## +# Record NetCDF output +nc_out_obs = True +nc_out_model = True +if nc_out_model or nc_out_obs: + ext = ".nc" +else: + ext = ".xml" +user_notes = 'Provenance and results' +debug = False + +# Generate plots +plot_model = True +plot_obs = True # optional + +# Additional settings +run_type = 'model_vs_obs' +figure_format = 'png' + +# Save interpolated model climatology ? +save_test_clims = True + +# Save Metrics Results in Single File ? +# option: 'y' or 'n', set to 'n' as we +# run pcmdi for each variable separately +metrics_in_single_file = 'n' + +# customize land/sea mask values +regions_values = {"land":100.,"ocean":0.} + +#setup template for land/sea mask (fixed) +modpath_lf = os.path.join( + 'fixed', + 'sftlf.%(model).nc' +) + +############################################ +#setup specific for mean climate metrics + +#case id +modver = case_id + +#always turn off +parallel = False + +#land/sea mask file (already generated) +generate_sftlf = False +sftlf_filename_template = modpath_lf + +# INTERPOLATION OPTIONS +# OPTIONS: '2.5x2.5' or an actual cdms2 grid object +target_grid = '2.5x2.5' +targetGrid = target_grid +target_grid_string = '2p5x2p5' +# OPTIONS: 'regrid2','esmf' +regrid_tool = 'esmf' +# OPTIONS: 'linear','conservative', only if tool is esmf +regrid_method = 'regrid2' +# OPTIONS: "regrid2","esmf" +regrid_tool_ocn = '' +# OPTIONS: 'linear','conservative', only if tool is esmf +regrid_method_ocn = ( 'conservative' ) + +####################################### +# DATA LOCATION: MODELS +# --------------------------------------------- +realization = "*" +test_data_set = [ product ] +test_data_path = 'climo' +# Templates for model climatology files +filename_template = '.'.join([ + mip, + exp, + '%(model)', + '%(realization)', + 'Amon', + '%(variable)', + period, + 'AC', + case_id, + 'nc' +]) + +#observation info +reference_data_path = 'climo_ref' +custom_observations = os.path.join( + 'pcmdi_diags', + '{}_{}_catalogue.json'.format( + 'climo_ref', + 'mean_climate')) + +#load caclulated regions for each variable +regions = json.load(open('regions.json')) + +#load predefined region information +regions_specs = json.load(open('regions_specs.json')) +for key in regions_specs.keys(): + if "domain" in regions_specs[key].keys(): + if "latitude" in regions_specs[key]['domain'].keys(): + regions_specs[key]['domain']['latitude'] = tuple( + regions_specs[key]['domain']['latitude'] + ) + if "longitude" in regions_specs[key]['domain'].keys(): + regions_specs[key]['domain']['longitude'] = tuple( + regions_specs[key]['domain']['longitude'] + ) + +####################################### +# DATA LOCATION: METRICS OUTPUT +metrics_output_path = os.path.join( + 'pcmdi_diags', + 'metrics_results', + 'mean_climate', + mip, + exp, + '%(case_id)' +) + +############################################################ +# DATA LOCATION: INTERPOLATED MODELS' CLIMATOLOGIES +diagnostics_output_path= os.path.join( + 'pcmdi_diags', + 'diagnostic_results', + 'mean_climate', + mip, + exp, + '%(case_id)' +) +test_clims_interpolated_output = diagnostics_output_path + diff --git a/sample_setups/external-setups/E3SM/mean_climate/reference_alias.json b/sample_setups/external-setups/E3SM/mean_climate/reference_alias.json new file mode 100755 index 000000000..1b1ebc921 --- /dev/null +++ b/sample_setups/external-setups/E3SM/mean_climate/reference_alias.json @@ -0,0 +1,341 @@ +{ + "rlds" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rldscs" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rlus" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsds" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsdscs" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + + "rsus" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsuscs": { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rstcre" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rltcre" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rlut" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rlutcs" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsdt" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsut" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rsutcs" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "rtmt" : { + "default" : "ceres_ebaf_v4.1", + "alternate" : "ceres_ebaf_v4.0", + "alternate1" : "ceres_ebaf_v2.8", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C" + }, + "pr" : { + "default" : "GPCP_v2.3", + "alternate" : "GPCP_v2.2", + "alternate1" : "GPCP_1DD", + "alternate2" : "ERA5", + "alternate3" : "MERRA2", + "alternate4" : "ERA-Interim", + "alternate5" : "NOAA-20C", + "alternate6" : "GPCP_v3.2" + }, + "prc" : { + "default" : "ERA5", + "alternate" : "NOAA-20C" + }, + "prsn" : { + "default" : "ERA5", + "alternate" : "NOAA-20C" + }, + "prw" : { + "default" : "ERA5", + "alternate" : "NOAA-20C", + "alternate1" : "MERRA2", + "alternate2" : "ERA-Interim", + "alternate3" : "NOAA-20C" + }, + "psl" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "ps" : { + "default" : "ERA5", + "alternate " : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "huss" : { + "default" : "MERRA2", + "alternate" : "NOAA-20C", + "alternate1" : "ERA5", + "alternate2" : "ERA-Interim" + }, + "ta" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "ua" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "va" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "hur" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "wap" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "zg" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "o3" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "hus" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "uas" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "vas" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "tauu" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "COREv2-Flux" + }, + "taux" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "COREv2-Flux" + }, + "tauv" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "COREv2-Flux" + }, + "tauy" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "COREv2-Flux" + }, + "tas" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C" + }, + "ts" : { + "default" : "ERA5", + "alternate" : "NOAA-20C", + "alternate1" : "HadISST2" + }, + "sst" : { + "default" : "ERA5", + "alternate" : "NOAA-20C", + "alternate1" : "HadISST2" + }, + "sfcWind" : { + "default" : "NOAA-20C", + "alternate" : "ERA5", + "alternate1" : "MERRA2", + "alternate2" : "ERA-Interim" + }, + "hfls" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "OAFlux" + }, + "hfss" : { + "default" : "ERA5", + "alternate" : "MERRA2", + "alternate1" : "ERA-Interim", + "alternate2" : "NOAA-20C", + "alternate3" : "OAFlux" + }, + "evspsbl" : { + "default" : "ERA5", + "alternate" : "NOAA-20C" + }, + "clt" : { + "default" : "ERA5", + "alternate3" : "NOAA-20C" + }, + "clwvi" : { + "default" : "ERA5", + "alternate" : "NOAA-20C" + }, + "clivi" : { + "default" : "ERA5", + "alternate" : "NOAA-20C" + }, + "tasmin" : { + "default" : "MERRA2" + }, + "tasmax" : { + "default" : "MERRA2" + }, + "sic" : { + "default" : "HadSST2" + }, + "tos" : { + "default" : "HadSST2" + }, + "zos" : { + "default" : "AVISO", + "alternate" : "HadISST" + }, + "sos" : { + "default" : "Aquarius", + "alternate" : "HadISST" + } +} diff --git a/sample_setups/external-setups/E3SM/mean_climate/regions_specs.json b/sample_setups/external-setups/E3SM/mean_climate/regions_specs.json new file mode 100755 index 000000000..811eb1e9c --- /dev/null +++ b/sample_setups/external-setups/E3SM/mean_climate/regions_specs.json @@ -0,0 +1,263 @@ +{ + "global": { + "domain": { "latitude":[-90.0, 90.0]} + }, + "NH": { + "domain": { "latitude":[0.0, 90.0]} + }, + "SH": { + "domain": { "latitude":[-90.0, 0]} + }, + "NHEX": { + "domain": { "latitude":[30.0, 90.0]} + }, + "SHEX": { + "domain": { "latitude":[-90.0, -30.0]} + }, + "TROPICS": { + "domain": { "latitude":[-30.0, 30.0]} + }, + "90S50S": { + "domain": { "latitude":[-90.0, -50.0]} + }, + "50S20S": { + "domain": { "latitude":[-50.0, -20.0]} + }, + "20S20N": { + "domain": { "latitude":[-20.0, 20.0]} + }, + "20N50N": { + "domain": { "latitude":[20.0, 50.0]} + }, + "50N90N": { + "domain": { "latitude":[50.0, 90.0]} + }, + "ocean_NH": { + "value": 0.0, + "domain": { "latitude":[0.0, 90.0]} + }, + "ocean_SH": { + "value": 0.0, + "domain": { "latitude":[-90.0, 0.0]} + }, + "land_NH": { + "value": 100, + "domain": { "latitude":[0.0, 90.0]} + }, + "land_SH": { + "value": 100, + "domain": { "latitude":[-90.0, 0.0]} + }, + "land_NHEX": { + "value": 100, + "domain": { "latitude":[30.0, 90.0]} + }, + "land_SHEX": { + "value": 100, + "domain": { "latitude":[-90.0, -30.0]} + }, + "land_TROPICS": { + "value": 100, + "domain": { "latitude":[-30.0, 30.0]} + }, + "land": { + "value": 100 + }, + "ocean_NHEX": { + "value": 0, + "domain": { "latitude":[30.0, 90.0]} + }, + "ocean_SHEX": { + "value": 0, + "domain": { "latitude":[-90.0, -30.0]} + }, + "ocean_TROPICS": { + "value": 0, + "domain": { "latitude":[30.0, 30.0]} + }, + "ocean": { + "value": 0 + }, + "ocean_50S50N": { + "value": 0.0, + "domain": { "latitude":[-50.0, 50.0]} + }, + "ocean_50S20S": { + "value": 0.0, + "domain": { "latitude":[-50.0, -20.0]} + }, + "ocean_20S20N": { + "value": 0.0, + "domain": { "latitude":[-20.0, 20.0]} + }, + "ocean_20N50N": { + "value": 0.0, + "domain": { "latitude":[20.0, 50.0]} + }, + "ocean_50N90N": { + "value": 0.0, + "domain": { "latitude":[50.0, 90.0]} + }, + "ocean_90S50S": { + "value": 0.0, + "domain": { "latitude":[-90.0, -50.0]} + }, + "NAM": { + "domain": { "latitude":[20.0, 90], + "longitude":[-180, 180]} + }, + "NAO": { + "domain": { "latitude":[20.0, 80], + "longitude":[-90, 40]} + }, + "SAM": { + "domain": { "latitude":[-20.0, -90], + "longitude":[0, 360]} + }, + "PSA1": { + "domain": { "latitude":[-20.0, -90], + "longitude":[0, 360]} + }, + "PSA2": { + "domain": { "latitude":[-20.0, -90], + "longitude":[0, 360]} + }, + "PNA": { + "domain": { "latitude":[20.0, 85], + "longitude":[120, 240]} + }, + "PDO": { + "domain": { "latitude":[20.0, 70], + "longitude":[110, 260]} + }, + "AMO": { + "domain": { "latitude":[0.0, 70], + "longitude":[-80, 0]} + }, + "AllMW": { + "domain": { "latitude":[-40.0, 45.0], + "longitude":[0.0, 360.0]} + }, + "AllM": { + "domain": { "latitude":[-45.0, 45.0], + "longitude":[0.0, 360.0]} + }, + "NAMM": { + "domain": { "latitude":[0.0, 45.0], + "longitude":[210.0, 310.0]} + }, + "SAMM": { + "domain": { "latitude":[-45.0, 0.0], + "longitude":[240.0, 330.0]} + }, + "NAFM": { + "domain": { "latitude":[0.0, 45.0], + "longitude":[310.0, 60.0]} + }, + "SAFM": { + "domain": { "latitude":[-45.0, 0.0], + "longitude":[0.0, 90.0]} + }, + "ASM": { + "domain": { "latitude":[0.0, 45.0], + "longitude":[60.0, 180.0]} + }, + "AUSM": { + "domain": { "latitude":[-45.0, 0.0], + "longitude":[90.0, 160.0]} + }, + "AIR": { + "domain": { "latitude":[7.0, 25.0], + "longitude":[65.0, 85.0]} + }, + "AUS": { + "domain": { "latitude":[-20.0, -10.0], + "longitude":[120.0, 150.0]} + }, + "Sahel": { + "domain": { "latitude":[13.0, 18.0], + "longitude":[-10.0, 10.0]} + }, + "GoG": { + "domain": { "latitude":[0.0, 5.0], + "longitude":[-10.0, 10.0]} + }, + "NAmo": { + "domain": { "latitude":[20.0, 37.0], + "longitude":[-112.0, -103.0]} + }, + "SAmo": { + "domain": { "latitude":[-20.0, 2.5], + "longitude":[-65.0, -40.0]} + }, + "Nino34": { + "value": 0.0, + "domain": { "latitude":[-5.0, 5.0], + "longitude":[190.0, 240.0]} + }, + "Nino3": { + "value": 0.0, + "domain": { "latitude":[-5.0, 5.0], + "longitude":[210.0, 270.0]} + }, + "Nino4": { + "value": 0.0, + "domain": { "latitude":[-5.0, 5.0], + "longitude":[160.0, 210.0]} + }, + "ONI": { + "value": 0.0, + "domain": { "latitude":[-5.0, 5.0], + "longitude":[190.0, 240.0]} + }, + "Nino12": { + "value": 0.0, + "domain": { "latitude":[-10.0, 0.0], + "longitude":[270.0, 280.0]} + }, + "AMMS": { + "value": 0.0, + "domain": { "latitude":[-15.0, -5.0], + "longitude":[-20.0, 10.0]} + }, + "AMMN": { + "value": 0.0, + "domain": { "latitude":[5.0, 15.0], + "longitude":[-50.0, -20.0]} + }, + "ATL3": { + "value": 0.0, + "domain": { "latitude":[-3.0, 3.0], + "longitude":[-20.0, 0.0]} + }, + "TSA": { + "value": 0.0, + "domain": { "latitude":[-20.0, 0.0], + "longitude":[-30.0, 10.0]} + }, + "TNA": { + "value": 0.0, + "domain": { "latitude":[5.5, 23.5], + "longitude":[302.5, 345.0]} + }, + "TIO": { + "value": 0.0, + "domain": { "latitude":[-15.0, 15.0], + "longitude":[40.0, 115.0]} + }, + "IODE": { + "value": 0.0, + "domain": { "latitude":[-10.0, 10.0], + "longitude":[50.0, 70.0]} + }, + "IODW": { + "value": 0.0, + "domain": { "latitude":[-10.0, 0.0], + "longitude":[90.0, 110.0]} + }, + "SOCN": { + "value": 0.0, + "domain": { "latitude":[-70.0, -50.0], + "longitude":[0.0, 360.0]} + } +} diff --git a/sample_setups/external-setups/E3SM/mean_climate/synthetic_metrics_list.json b/sample_setups/external-setups/E3SM/mean_climate/synthetic_metrics_list.json new file mode 100755 index 000000000..3d75db8d1 --- /dev/null +++ b/sample_setups/external-setups/E3SM/mean_climate/synthetic_metrics_list.json @@ -0,0 +1,38 @@ +{ + "mean_climate" : { + "cor_xy" : { + "name" : "Pattern Corr.", + "type" : "portrait", + "season" : ["djf", "mam", "jja", "son"] + }, + "rms_xy" : { + "name" : "RMSE Normalized by Median", + "type" : "portrait", + "season" : ["djf", "mam", "jja", "son"] + }, + "rms_xyt" : { + "name" : "Spatiotemporal RMSE", + "type" : "parcoord", + "season" : ["ann"] + } + }, + "variability_modes" : { + "rms" : { + "name" : "RMSE Normalized by Median", + "type" : ["portrait","parcoord"], + "season" : [] + }, + "rmsc" : { + "name" : "CenteredRMSE Normalized by Median", + "type" : ["portrait","parcoord"], + "season" : [] + }, + "stdv_pc_ratio_to_obs" : { + "name" : "Amplitude Ratio to Reference", + "type" : ["portrait","parcoord"], + "season" : [] + } + + } +} + diff --git a/sample_setups/external-setups/E3SM/util/link_observation.py b/sample_setups/external-setups/E3SM/util/link_observation.py new file mode 100755 index 000000000..a9cc8d1de --- /dev/null +++ b/sample_setups/external-setups/E3SM/util/link_observation.py @@ -0,0 +1,103 @@ +import os +import re +import glob +import json +import time +import datetime +import xcdat as xc +import numpy as np +import shutil + +import pcmdi_metrics +from pcmdi_metrics.io import ( + xcdat_open +) + +from pcmdi_zppy_util import( + derive_var, +) + +model_name = 'obs.historical.%(model).00.Amon' +variables = 'pr,prw,psl,rlds,rldscs,rltcre,rstcre,rsus,rsuscs,rlus,rlut,rlutcs,rsds,rsdscs,rsdt,rsut,rsutcs,rtmt,sfcWind,tas,tauu,tauv,ts,ta-200,ta-850,ua-200,ua-850,va-200,va-850,zg-500'.split(",") +obs_sets = 'default'.split(",") +ts_dir_ref_source = '/lcrc/soft/climate/e3sm_diags_data/obs_for_e3sm_diags/time-series' + +# variable map from observation to cmip +altobs_dic = { "pr" : "PRECT", + "sst" : "ts", + "sfcWind" : "si10", + "taux" : "tauu", + "tauy" : "tauv", + "rltcre" : "toa_cre_lw_mon", + "rstcre" : "toa_cre_sw_mon", + "rtmt" : "toa_net_all_mon"} + +obs_dic = json.load(open('reference_alias.json')) + +######################################## +#first loop: link data to work directory +######################################## +for i,vv in enumerate(variables): + if "_" in vv or "-" in vv: + varin = re.split("_|-", vv)[0] + else: + varin = vv + if len(obs_sets) > 1 and len(obs_sets) == len(variables): + obsid = obs_sets[i] + else: + obsid = obs_sets[0] + + obsname = obs_dic[varin][obsid] + if "ceres_ebaf" in obsname: + obsstr = obsname.replace("_","*").replace("-","*") + else: + obsstr = obsname + + fpaths = sorted(glob.glob(os.path.join(ts_dir_ref_source,obsstr,varin+"_*.nc"))) + if (len(fpaths) > 0) and (os.path.exists(fpaths[0])): + template = fpaths[0].split("/")[-1] + yms = template.split("_")[-2][0:6] + yme = template.split("_")[-1][0:6] + obs = obsname.replace(".","_") + out = os.path.join( + 'obs_link', + '{}.{}.{}-{}.nc'.format( + model_name.replace('%(model)',obs), + varin,yms,yme) + ) + if not os.path.exists(out): + os.symlink(fpaths[0],out) + elif varin in altobs_dic.keys(): + varin1 = altobs_dic[varin] + fpaths = sorted(glob.glob( + os.path.join(ts_dir_ref_source,obsstr,varin1+"_*.nc")) + ) + if (len(fpaths) > 0) and (os.path.exists(fpaths[0])): + template = fpaths[0].split("/")[-1] + yms = template.split("_")[-2][0:6] + yme = template.split("_")[-1][0:6] + obs = obsname.replace(".","_") + out = os.path.join( + 'obs_link', + '{}.{}.{}-{}.nc'.format( + model_name.replace('%(model)',obs), + varin,yms,yme) + ) + ds = xcdat_open(fpaths[0]) + ds = ds.rename(name_dict={varin1:varin}) + ds.to_netcdf(out) + +##################################################################### +#second loop: check and process derived quantities +#note: these quantities are possibly not included as default in cmip +##################################################################### +for vv in enumerate(variables): + if vv in ['rltcre','rstcre']: + fpaths = sorted(glob.glob( + os.path.join('obs_link',"*"+vv+"_*.nc")) + ) + if (len(fpaths) < 1) and (vv == 'rstcre'): + derive_var('obs_link',vv,{'rsutcs':1,'rsut':-1},model_name) + elif (len(fpaths) < 1) and (vv == 'rltcre'): + derive_var('obs_link',vv,{'rlutcs':1,'rlut':-1},model_name) + diff --git a/sample_setups/external-setups/E3SM/util/pcmdi_zppy_util.py b/sample_setups/external-setups/E3SM/util/pcmdi_zppy_util.py new file mode 100755 index 000000000..8765c2bd4 --- /dev/null +++ b/sample_setups/external-setups/E3SM/util/pcmdi_zppy_util.py @@ -0,0 +1,1019 @@ +import os +import glob +import re +import json +import time +import datetime +import xcdat as xc +import numpy as np +import pandas as pd +import shutil +import psutil +import subprocess +import matplotlib.pyplot as plt + +import collections +from collections import OrderedDict + +from itertools import chain +from subprocess import Popen, PIPE, call + +import pcmdi_metrics + +from pcmdi_metrics.io import ( + xcdat_open +) + +from pcmdi_metrics.utils import ( + sort_human, + create_land_sea_mask +) + +from pcmdi_metrics.graphics import ( + Metrics, + normalize_by_median, + parallel_coordinate_plot, + portrait_plot, +) + +def childCount(): + current_process = psutil.Process() + children = current_process.children() + return(len(children)) + +def parallel_jobs(cmds,num_workers): + procs = [] + # Start multiple subprocesses + for i,p in enumerate(cmds): + proc = Popen(p, stdout=PIPE, shell=True) + procs.append(proc) + if (len(procs) >= num_workers) or (i == len(cmds)-1): + print('running {} subprocesses'.format(childCount())) + for proc in procs: + stdout, error = proc.communicate() + rcode = proc.returncode + if error: + exit("ERROR: process {} failed".format(p)) + time.sleep(0.25) + procs = [] + + return stdout,error,rcode + +def serial_jobs(cmds,num_workers): + for i,p in enumerate(cmds): + print('running %s' % (str(p))) + proc = Popen(p, stdout=PIPE, shell=True) + stdout, error = proc.communicate() + rcode = proc.returncode + if error: + exit("ERROR: process {} failed".format(p)) + return stdout,error,rcode + +def derive_var(path,vout,var_dic,fname): + for i,var in enumerate(var_dic.keys()): + fpath = sorted(glob.glob(os.path.join(path,"*."+var+".*.nc"))) + df = xcdat_open(fpath[0]) + if i == 0: + template = fpath[0].split("/")[-1] + #construct a copy of file for derived variable + out = os.path.join(path,template.replace(".{}.".format(var),".{}.".format(vout))) + shutil.copy(fpath[0],out) + ds = xcdat_open(fpath[0]) + ds = ds.rename_vars({var:vout}) + ds[vout].data = ds[vout].data * var_dic[var] + else: + ds[vout].data = ds[vout].data + df[var].data * var_dic[var] + ds.to_netcdf(out) + + return + +def collect_data_info(test,test_set,refr,refr_set,variables,outset,outdir): + #collect variables when both model and observations are available + test_dic,refr_dic = OrderedDict(),OrderedDict() + for i,var in enumerate(variables): + if "_" in var or "-" in var: + varin = re.split("_|-", var)[0] + else: + varin = var + test_path = sorted(glob.glob(os.path.join(test,"*.{}.*.nc".format(varin)))) + refr_path = sorted(glob.glob(os.path.join(refr,"*.{}.*.nc".format(varin)))) + if (len(test_path) > 0) and (len(refr_path) > 0): + if (os.path.exists(test_path[0])) and (os.path.exists(refr_path[0])): + for j,path in enumerate([test_path[0],refr_path[0]]): + fname = path.split("/")[-1] + model = fname.split(".")[2] + sbdic = { "mip" : fname.split(".")[0], + "exp" : fname.split(".")[1], + "model" : fname.split(".")[2], + "realization" : fname.split(".")[3], + "tableID" : fname.split(".")[4], + "yymms" : fname.split(".")[6].split("-")[0], + "yymme" : fname.split(".")[6].split("-")[1], + "var_in_file" : varin, + "var_name" : var, + "yymms" : fname.split(".")[6].split("-")[0], + "yymme" : fname.split(".")[6].split("-")[1], + "var_in_file" : varin, + "var_name" : var, + "file_path" : path, + "template" : fname + } + if j == 0: + if varin not in test_dic.keys(): + test_dic[varin] = {} + if len(test_set) != len(variables): + kset = test_set[0] + else: + kset = test_set[i] + test_dic[varin]['set'] = kset + test_dic[varin][kset] = model + test_dic[varin][model] = sbdic + else: + if varin not in refr_dic.keys(): + refr_dic[varin] = {} + if len(refr_set) != len(variables): + kset = refr_set[0] + else: + kset = refr_set[i] + refr_dic[varin]['set'] = kset + refr_dic[varin][kset] = model + refr_dic[varin][model] = sbdic + + # Save test and obs/reference data information for next step + for i,group in enumerate([test,refr]): + if i == 0: + out_dic = test_dic + else: + out_dic = refr_dic + out_file = os.path.join(outdir,'{}_{}_catalogue.json'.format(group,outset)) + json.dump(out_dic,open(out_file, "w"),sort_keys=False,indent=4,separators=(",",": ")) + + return test_dic,refr_dic + +def variable_region(regions,variables): + regv_dic = OrderedDict() + for var in variables: + if "_" in var or "-" in var: + vkey = re.split("_|-", var)[0] + else: + vkey = var + regv_dic[vkey] = regions + #save region info dictionary + json.dump(regv_dic, + open('regions.json', "w"), + sort_keys=False, + indent=4, + separators=(",", ": ")) + return + +def enso_obsvar_dict(obs_dic,variables): + #orgnize observation for enso driver + refr_dic = OrderedDict() + for var in variables: + if "_" in var or "-" in var: + vkey = re.split("_|-", var)[0] + else: + vkey = var + refset = obs_dic[vkey]['set'] + refname = obs_dic[vkey][refset] + #data file in model->var sequence + if refname not in refr_dic.keys(): + refr_dic[refname] = {} + refr_dic[refname][vkey] = obs_dic[vkey][refname] + + #save data file dictionary + json.dump(refr_dic, + open('obs_catalogue.json', "w"), + sort_keys=False, + indent=4, + separators=(",", ": ")) + + return + +def enso_obsvar_lmsk(obs_dic,variables): + #orgnize observation landmask for enso driver + relf_dic = OrderedDict() + for var in variables: + if "_" in var or "-" in var: + vkey = re.split("_|-", var)[0] + else: + vkey = var + refset = obs_dic[vkey]['set'] + refname = obs_dic[vkey][refset] + #land/sea mask + if refname not in relf_dic.keys(): + relf_dic[refname] = os.path.join( + 'fixed', + 'sftlf.{}.nc'.format(refname)) + + #save data file dictionary + json.dump(relf_dic, + open('obs_landmask.json', "w"), + sort_keys=False, + indent=4, + separators=(",", ": ")) + + return + +def shift_row_to_bottom(df, index_to_shift): + idx = [i for i in df.index if i != index_to_shift] + return df.loc[idx + [index_to_shift]] + +def merge_data(model_lib,cmip_lib,model_name): + model_lib,cmip_lib = check_regions(model_lib,cmip_lib) + merge_lib = cmip_lib.merge(model_lib) + merge_lib = check_units(merge_lib) + for stat in merge_lib.df_dict: + for season in merge_lib.df_dict[stat]: + for region in merge_lib.df_dict[stat][season]: + highlight_models = [] + df = merge_lib.df_dict[stat][season][region] + for model in df["model"].tolist(): + if "e3sm" in model.lower(): + highlight_models.append(model) + if model in model_name: + idxs = df[df.iloc[:, 0] == model].index + df.loc[idxs, "model"] = model_name + highlight_models.append(model_name) + for model in highlight_models: + for idx in df[df.iloc[:, 0] == model].index: + df = shift_row_to_bottom(df, idx) + merge_lib.df_dict[stat][season][region] = df.fillna(value=np.nan) + del(df) + return merge_lib + +def check_badvals(data_lib): + var_model={"E3SM-1-0" : "ta-850", + "E3SM-1-1-ECA": "ta-850", + "CIESM" : "pr"} + # loop data metrics and check bad values + for stat in data_lib.df_dict: + for season in data_lib.df_dict[stat]: + for region in data_lib.df_dict[stat][season]: + df = pd.DataFrame(data_lib.df_dict[stat][season][region]) + for i, model in enumerate(df["model"].tolist()): + if model in var_model.keys(): + idx = df[df.iloc[:, 0] == model].index + df.loc[idx, var_model[model]] = np.nan + #rewrite the data with revised values + data_lib.df_dict[stat][season][region] = df + del(df) + return data_lib + +def check_regions(data_lib,refr_lib): + regions = [x for x in data_lib.regions if x in refr_lib.regions] + for kk in range(2): + if kk == 0: + df_dict = refr_lib.df_dict.copy() + else: + df_dict = data_lib.df_dict.copy() + #only keep shared regions + for stat in df_dict: + for season in df_dict[stat]: + sets_dict = dict((k, df_dict[stat][season][k]) for k in regions) + if kk == 0: + refr_lib.df_dict[stat][season] = sets_dict + else: + data_lib.df_dict[stat][season] = sets_dict + del(sets_dict) + + #reassign regions + refr_lib.regions,data_lib.regions = regions,regions + + return data_lib,refr_lib + +def check_references(data_dict): + reference_alias = {'CERES-EBAF-4-1': 'ceres_ebaf_v4_1', + 'CERES-EBAF-4-0': 'ceres_ebaf_v4_0', + 'CERES-EBAF-2-8': 'ceres_ebaf_v2_8', + 'GPCP-2-3' : 'GPCP_v2_3', + 'GPCP-2-2' : 'GPCP_v2_2', + 'GPCP-3-2' : 'GPCP_v3_2', + 'NOAA_20C' : 'NOAA-20C', + 'ERA-INT' : 'ERA-Interim', + 'ERA-5' : 'ERA5'} + for key,values in data_dict.items(): + if values != None: + for i,value in enumerate(values): + if value in reference_alias.keys(): + values[i] = reference_alias[value] + data_dict[key] = values + return data_dict + +def check_units(data_lib): + # we define fixed sets of variables used for final plotting. + units_all = { + "prw" : "[kg m$^{-2}$]", "pr" : "[mm d$^{-1}$]", "prsn" : "[mm d$^{-1}$]", + "prc" : "[mm d$^{-1}$]", "hfls" : "[W m$^{-2}$]", "hfss" : "[W m$^{-2}$]", + "clivi" : "[kg $m^{-2}$]", "clwvi" : "[kg $m^{-2}$]", "psl" : "[Pa]", + "rlds" : "[W m$^{-2}$]", "rldscs": "[W $m^{-2}$]", "evspsbl": "[kg m$^{-2} s^{-1}$]", + "rtmt" : "[W m$^{-2}$]", "rsdt" : "[W m$^{-2}$]", "rlus" : "[W m$^{-2}$]", + "rluscs": "[W m$^{-2}$]", "rlut" : "[W m$^{-2}$]", "rlutcs" : "[W m$^{-2}$]", + "rsds" : "[W m$^{-2}$]", "rsdscs": "[W m$^{-2}$]", "rstcre" : "[W m$^{-2}$]", + "rltcre": "[W m$^{-2}$]", "rsus" : "[W m$^{-2}$]", "rsuscs" : "[W m$^{-2}$]", + "rsut" : "[W m$^{-2}$]", "rsutcs": "[W m$^{-2}$]", "ts" : "[K]", + "tas" : "[K]", "tauu" : "[Pa]", "tauv" : "[Pa]", + "zg-500": "[m]", "ta-200": "[K]", "sfcWind": "[m s$^{-1}$]", + "ta-850": "[K]", "ua-200": "[m s$^{-1}$]", "ua-850" : "[m s$^{-1}$]", + "va-200": "[m s$^{-1}$]", "va-850": "[m s$^{-1}$]", "uas" : "[m s$^{-1}$]", + "vas" : "[m s$^{-1}$]", "tasmin": "[K]", "tasmax" : "[K]", + "clt" : "[%]"} + + common_vars = [x for x in data_lib.var_list if x in units_all.keys()] + #special case + if 'rtmt' not in common_vars: + if ('rt' in data_lib.var_list) or ('rmt' in data_lib.var_list): + common_vars.append('rtmt') + + #collect unit list + common_unts = [units_all[x] for x in common_vars] + + #collect reference list + reflist = data_lib.var_ref_dict.copy() + for var in reflist: + if var not in common_vars: + if var in ['rt','rmt']: + data_lib.var_ref_dict['rtmt'] = data_lib.var_ref_dict.pop(var) + else: + data_lib.var_ref_dict.pop(var) + data_lib.var_ref_dict = check_references(data_lib.var_ref_dict) + #now clean up data to exclude vars not in common lists + for stat in data_lib.df_dict: + for season in data_lib.df_dict[stat]: + for region in data_lib.df_dict[stat][season]: + df = data_lib.df_dict[stat][season][region] + if 'rt' in df.columns: + df['rtmt'] = df['rt'] + elif 'rmt' in df.columns: + df['rtmt'] = df['rmt'] + for var in df.columns[3:]: + if var not in common_vars: + df = df.drop(var,axis=1) + data_lib.df_dict[stat][season][region] = df + del(df) + + data_lib.var_list = common_vars + data_lib.var_unit_list = common_unts + + return data_lib + +def collect_clim_metrics(parameter): + #merge data to an exisiting cmip base + cmip_files = glob.glob(os.path.join( + parameter['cmip_path'], + parameter['cmip_name'].split(".")[0], + parameter['cmip_name'].split(".")[1], + parameter['cmip_name'].split(".")[2], + "*.{}.json".format(parameter['cmip_name'].split(".")[2]))) + if len(cmip_files) > 0 and os.path.exists(cmip_files[0]): + print('CMIP PCMDI DIAGs for Sythetic Metrics Found, Read data...') + cmip_lib = Metrics(cmip_files) + cmip_lib = check_badvals(cmip_lib) + cmip_lib = check_units(cmip_lib) + else: + exit("Warning: CMIP PCMDI DIAGs for Sythetic Metrics Not Found,....") + + model_name = '-'.join([ + parameter['test_name'].split(".")[2], + parameter['test_name'].split(".")[3]]) + model_files = glob.glob(os.path.join( + parameter['test_path'], + "*_{}.json".format(parameter['case_id']))) + if len(model_files) > 0 and os.path.exists(model_files[0]): + print('{} PCMDI DIAGs for Sythetic Metrics Found, Read data...'.format(model_name)) + model_lib = Metrics(model_files) + model_lib = check_units(model_lib) + else: + exit("Warning: Model PCMDI DIAGs for Sythetic Metrics Not Found,....") + + #merge model data with reference cmip data + merge_lib = merge_data(model_lib,cmip_lib,model_name) + + return merge_lib + +def movs_load_file(file_lists,modes): + json_lib = dict() + for mode in modes: + if mode in ['PSA1', 'NPO', 'NPGO']: + eof = 'EOF2' + elif mode in ['PSA2']: + eof = 'EOF3' + else: + eof = 'EOF1' + for json_file in file_lists: + if mode in json_file and eof in json_file: + with open(json_file) as fj: + dict_temp = json.load(fj)['RESULTS'] + json_lib[mode] = dict_temp + return json_lib + +def movs_dict2df(movs_dict,stat,modes): + models = sorted(list(movs_dict['NAM'].keys())) + df = pd.DataFrame() + df['model'] = models + df['num_runs'] = np.nan + mode_season_list = list() + for mode in modes: + if mode in ['PDO','NPGO']: + seasons = ['monthly'] + elif mode in ['AMO']: + seasons = ['yearly'] + else: + seasons = ['DJF','MAM','JJA','SON'] + for season in seasons: + df[mode+"_"+season] = np.nan + mode_season_list.append(mode+"_"+season) + for index, model in enumerate(models): + if mode in movs_dict.keys() and model in list(movs_dict[mode].keys()): + runs = sort_human(list(movs_dict[mode][model].keys())) + stat_run_list = list() + for run in runs: + stat_run = ( + movs_dict[mode][model][run]['defaultReference'][mode][season]['cbf'][stat] + ) + stat_run_list.append(stat_run) + stat_model = np.average(np.array(stat_run_list)) + num_runs = len(runs) + df.at[index, mode+"_"+season] = stat_model + if np.isnan(df.at[index, 'num_runs']): + df.at[index, 'num_runs'] = num_runs + else: + stat_model = np.nan + num_runs = 0 + # assign missing values for modes not in cmip datasets + df.at[index, mode+"_"+season] = stat_model + df.at[index, 'num_runs'] = 0 + + return df, mode_season_list + +def collect_movs_metrics(parameter): + #merge data to an exisiting cmip base + cmip_files = glob.glob(os.path.join( + parameter['cmip_path'], + parameter['cmip_name'].split(".")[0], + parameter['cmip_name'].split(".")[1], + parameter['cmip_name'].split(".")[2], + "*/*/var_mode_*.json")) + if len(cmip_files) > 0 and os.path.exists(cmip_files[0]): + print('CMIP PCMDI DIAGs for Sythetic Metrics Found, Read data...') + cmip_lib = movs_load_file(cmip_files,parameter['movs_mode']) + else: + exit("Warning: CMIP PCMDI DIAGs for Sythetic Metrics Not Found,....") + + model_name = '-'.join([ + parameter['test_name'].split(".")[2], + parameter['test_name'].split(".")[3]]) + model_files = glob.glob(os.path.join( + parameter['test_path'], + "*_{}.json".format(parameter['case_id']))) + if len(model_files) > 0 and os.path.exists(model_files[0]): + print('{} PCMDI DIAGs for Sythetic Metrics Found, Read data...'.format(model_name)) + model_lib = movs_load_file(model_files,parameter['movs_mode']) + else: + exit("Warning: Model PCMDI DIAGs for Sythetic Metrics Not Found,....") + + merge_lib = dict() + for stat in parameter['diag_vars'].keys(): + cmip_df, mode_season_list = movs_dict2df(cmip_lib,stat,parameter['movs_mode']) + model_df, mode_season_list = movs_dict2df(model_lib,stat,parameter['movs_mode']) + merge_df = pd.concat([cmip_df, model_df],ignore_index=True) + for model in merge_df["model"].tolist(): + if "e3sm" in model.lower(): + for idx in merge_df[merge_df.iloc[:, 0] == model].index: + merge_df = shift_row_to_bottom(merge_df, idx) + for model in merge_df["model"].tolist(): + if model in model_name: + for idx in merge_df[merge_df.iloc[:, 0] == model].index: + merge_df.loc[idx,"model"] = model_name + merge_df = shift_row_to_bottom(merge_df, idx) + merge_lib[stat] = merge_df + del(cmip_df,model_df,merge_df) + + return merge_lib,mode_season_list + +def create_data_lmask(test,refr,subset,fixed_dir): + #loop each group and process land/mask if not exist + for group in [test,refr]: + dic_file = os.path.join( + 'pcmdi_diags', + '{}_{}_catalogue.json'.format(group,subset) + ) + data_dic = json.load(open(dic_file)) + for var in data_dic.keys(): + mdset = data_dic[var]['set'] + model = data_dic[var][mdset] + mpath = data_dic[var][model]['file_path'] + mpath_lf = os.path.join( + fixed_dir, + 'sftlf.{}.nc'.format(model) + ) + if not os.path.exists(fixed_dir): + os.makedirs(fixed_dir) + # generate land/sea mask if not exist + if not os.path.exists(mpath_lf): + ds = xcdat_open(mpath, decode_times=True) + ds = ds.bounds.add_missing_bounds() + try: + lf_array = create_land_sea_mask(ds, method="regionmask") + print("land mask is estimated using regionmask method.") + except Exception: + lf_array = create_land_sea_mask(ds, method="pcmdi") + print("land mask is estimated using pcmdi method.") + lf_array = lf_array * 100.0 + lf_array.attrs['long_name']= "land_area_fraction" + lf_array.attrs['units'] = "%" + lf_array.attrs['id'] = "sftlf" # Rename + ds_lf = lf_array.to_dataset(name='sftlf').compute() + ds_lf = ds_lf.bounds.add_missing_bounds() + ds_lf.fillna(1.0e20) + ds_lf.attrs['model'] = model + ds_lf.attrs['associated_files'] = mpath + ds_lf.attrs['history'] = "File processed: " + datetime.datetime.now().strftime("%Y%m%d") + comp = dict(_FillValue=1.0e20,zlib=True,complevel=5) + encoding = {var: comp for var in list(ds_lf.data_vars)+list(ds_lf.coords)} + ds_lf.to_netcdf(mpath_lf,encoding=encoding) + del(ds,ds_lf,lf_array) + + return + +def archive_data( + region,stat,season,data_dict, + model_name,var_names,var_units, + outdir + ): + outdic = pd.DataFrame(data_dict) + for var in list(outdic.columns.values[3:]): + if var not in var_names: + outdic = outdic.drop(columns=[var]) + elif var_units != None: + # replace the variable with the name + units + outdic.columns.values[outdic.columns.values.tolist().index(var)] = ( + var_units[var_names.index(var)]) + # save data to .csv file + if not os.path.exists(outdir): + os.makedirs(outdir) + outfile = "{}_{}_{}_{}.csv".format(stat,region,season,model_name) + outdic.to_csv(os.path.join(outdir,outfile)) + return + +def drop_vars(data_dict,var_names,var_units): + #drop data if all is NaNs + for column in data_dict.columns: + if column not in ['model','run','model_run','num_runs']: + nnans = data_dict[column].isnull().sum() + nsize = data_dict[column].size + if nnans > 0.9*nsize: + data_dict = data_dict.drop(column, axis=1) + index = var_names.index(column) + var_names.remove(var_names[index]) + if var_units != None: + var_units.remove(var_units[index]) + return data_dict, var_names, var_units + +def variability_modes_plot_driver( + metric,stat,model_name, + metric_dict,df_dict, + mode_season_list, + save_data,out_path + ): + """Driver Function for the modes variability metrics plot""" + season = "mon" + for mtype in metric_dict['type']: + if mtype == "portrait": + print("Processing Portrait Plots for {} {}....".format(metric,stat)) + if stat not in ["stdv_pc_ratio_to_obs"]: + data_nor = normalize_by_median( + df_dict[mode_season_list].to_numpy().T, axis=1) + else: + data_nor = df_dict[mode_season_list].to_numpy().T + if save_data: + df_dict[mode_season_list] = data_nor.T + outdir = os.path.join(out_path,metric) + archive_data(metric,stat,season,df_dict,model_name, + mode_season_list,None,outdir) + run_list = df_dict['model'].to_list() + stat_name = metric_dict['name'] + portrait_metric_plot(metric,stat,season,data_nor, + stat_name,model_name,mode_season_list, + run_list,out_path) + elif mtype == "parcoord": + print("Processing Parallel Coordinate Plots for {} {}....".format(metric,stat)) + #drop data if all is NaNs + data_dict,var_names,var_units = drop_vars(df_dict.copy(),mode_season_list.copy(),None) + if save_data: + outdir = os.path.join(out_path,metric) + archive_data(metric,stat,season,data_dict,model_name, + mode_season_list,None,outdir) + run_list = data_dict['model'].to_list() + stat_name = metric_dict['name'] + parcoord_metric_plot(metric,stat,season,data_dict, + stat_name,model_name, + var_names,var_units, + run_list,out_path) + + return + +def mean_climate_plot_driver( + metric,stat,regions,model_name, + metric_dict,df_dict, + var_list,var_unit_list, + save_data,out_path + ): + """Driver Function for the mean climate metrics plot""" + for region in regions: + if metric_dict['type'] == "portrait": + print("Processing Portrait Plots for {} {} {}....".format(metric,region,stat)) + var_names = sorted(var_list.copy()) + #label information + var_units = [] + for i,var in enumerate(var_names): + index = var_list.index(var) + var_units.append(var_unit_list[index]) + data_nor = dict() + for season in metric_dict['season']: + data_dict = df_dict[season][region].copy() + if stat == "cor_xy": + data_nor[season] = data_dict[var_names].to_numpy().T + else: + data_nor[season] = normalize_by_median( + data_dict[var_names].to_numpy().T, axis=1) + if save_data: + outdir = os.path.join(out_path,metric,region) + outdic = data_dict.drop(columns=["model_run"]).copy() + outdic[var_names] = data_nor[season].T + archive_data(region,stat,season,data_dict,model_name, + var_names,var_units,outdir) + run_list = data_dict['model'].to_list() + stat_name = metric_dict['name'] + outdir = os.path.join(out_path,metric) + portrait_metric_plot(region,stat,metric,data_nor, + stat_name,model_name,var_names, + run_list,outdir) + + elif metric_dict['type'] == "parcoord": + print("Processing Parallel Coordinate Plots for {} {} {}....".format(metric,region,stat)) + for season in metric_dict['season']: + #drop data if all is NaNs + data_dict,var_names,var_units = drop_vars( + df_dict[season][region].copy(), + var_list.copy(), + var_unit_list.copy()) + if save_data: + outdir = os.path.join(out_path,metric,region) + outdic = data_dict.drop(columns=["model_run"]).copy() + archive_data(region,stat,season,outdic,model_name, + var_list,var_unit_list,outdir) + run_list = data_dict['model'].to_list() + stat_name = metric_dict['name'] + outdir = os.path.join(out_path,metric) + parcoord_metric_plot(region,stat,metric,data_dict, + stat_name,model_name, + var_names,var_units, + run_list,outdir) + return + +def parcoord_metric_plot( + region,stat,group,data_dict, + stat_name,model_name, + var_names,var_units, + model_list,out_path + ): + """ Function for parallel coordinate plots """ + fontsize = 18 + figsize = (40, 18) + shrink = 0.8 + legend_box_xy = (1.08, 1.18) + legend_box_size = 4 + legend_lw = 1.5 + legend_fontsize = fontsize * 0.8 + legend_ncol = int(7 * figsize[0] / 40.0) + legend_posistion = (0.50, -0.14) + # hide markers for CMIP models + identify_all_models = False + # colors for highlight lines + xcolors = ["#000000","#e41a1c","#ff7f00","#4daf4a","#f781bf", + "#a65628","#984ea3","#999999","#377eb8","#dede00"] + + highlight_model1 = [] + for model in data_dict['model'].to_list(): + if "e3sm" in model.lower(): + highlight_model1.append(model) + elif model in model_name: + highlight_model1.append(model_name) + + # ensemble mean for CMIP group + irow_sub = data_dict[data_dict['model'] == highlight_model1[0]].index[0] + data_dict.loc["CMIP MMM"] = data_dict[:irow_sub].mean( + numeric_only=True, skipna=True) + data_dict.at["CMIP MMM", "model"] = "CMIP MMM" + data_dict.loc["E3SM MMM"] = data_dict[irow_sub:].mean( + numeric_only=True, skipna=True) + data_dict.at["E3SM MMM", "model"] = "E3SM MMM" + + model_list = data_dict['model'].to_list() + highlight_model2 = data_dict['model'].to_list()[-3:] + + var_name1 = sorted(var_names.copy()) + #label information + var_labels = [] + for i,var in enumerate(var_name1): + index = var_names.index(var) + if var_units != None: + var_labels.append(var_names[index] + "\n" + var_units[index]) + else: + var_labels.append(var_names[index]) + + #final plot data + data_var = data_dict[var_name1].to_numpy() + + xlabel = "Metric" + ylabel = '{} ({})'.format(stat_name,stat.upper()) + # colors for highlight lines + lncolors = xcolors[1 : len(highlight_model2)] + [xcolors[0]] + color_map = "tab20_r" + + if "mean_climate" in [group,region]: + title = "Model Performance of Annual Climatology ({}, {})".format( + stat.upper(), region.upper()) + elif "variability_modes" in [group,region]: + title = "Model Performance of Modes Variability ({})".format( + stat.upper()) + elif "enso" in [group,region]: + title = "Model Performance of ENSO ({})".format( + stat.upper()) + + fig,ax = parallel_coordinate_plot( + data_var, + var_labels, + model_list, + model_names2=highlight_model1, + group1_name="CMIP6", + group2_name="E3SM", + models_to_highlight=highlight_model2, + models_to_highlight_colors=lncolors, + models_to_highlight_labels=highlight_model2, + identify_all_models=identify_all_models, + vertical_center="median", + vertical_center_line=True, + title=title, + figsize=figsize, + colormap=color_map, + show_boxplot=False, + show_violin=True, + violin_colors=("lightgrey", "pink"), + legend_ncol=legend_ncol, + legend_bbox_to_anchor=legend_posistion, + legend_fontsize=fontsize * 0.85, + xtick_labelsize=fontsize * 0.95, + ytick_labelsize=fontsize * 0.95, + logo_rect=[0, 0, 0, 0], + logo_off=True) + + ax.set_xlabel(xlabel, fontsize = fontsize * 1.1) + ax.set_ylabel(ylabel, fontsize = fontsize * 1.1) + ax.set_title(title, fontsize = fontsize * 1.1) + + # Save figure as an image file + outdir = os.path.join(out_path,region) + if not os.path.exists(outdir): + os.makedirs(outdir) + outfile = "{}_{}_parcoord_{}.png".format(stat,region,group) + fig.savefig(os.path.join(outdir,outfile),facecolor="w", bbox_inches="tight") + plt.close(fig) + + return + +def portrait_metric_plot( + region,stat,group,data_dict, + stat_name,model_name, + var_list,model_list, + out_path + ): + # process figure + fontsize = 20 + add_vertical_line = True + figsize = (40, 18) + legend_box_xy = (1.08, 1.18) + legend_box_size = 4 + legend_lw = 1.5 + shrink = 0.8 + legend_fontsize = fontsize * 0.8 + + if group == "mean_climate": + # data for final plot + data_all_nor = np.stack( + [data_dict["djf"], data_dict["mam"], data_dict["jja"], data_dict["son"]] + ) + legend_on = True + legend_labels = ["DJF", "MAM", "JJA", "SON"] + else: + data_all_nor = data_dict + legend_on = False + legend_labels = [] + + lable_colors = [] + highlight_models = [] + for model in model_list: + if "e3sm" in model.lower(): + highlight_models.append(model) + lable_colors.append("#5170d7") + elif model in model_name: + highlight_models.append(model_name) + lable_colors.append("#FC5A50") + else: + lable_colors.append("#000000") + + if stat in ["cor_xy"]: + var_range = (0, 1.0) + cmap_color = "viridis" + cmap_bounds = np.linspace(0,1,21) + elif stat in ["stdv_pc_ratio_to_obs"]: + var_range = (0.5, 1.5) + cmap_color = 'jet' + cmap_bounds = [0.5, 0.7, 0.9, 1.1, 1.3, 1.5] + cmap_bounds = [r/10 for r in range(5, 16, 1)] + else: + var_range = (-0.5, 0.5) + cmap_color = "RdYlBu_r" + cmap_bounds = np.linspace(-0.5,0.5,11) + + fig, ax, cbar = portrait_plot( + data_all_nor, + xaxis_labels=model_list, + yaxis_labels=var_list, + cbar_label=stat_name, + cbar_label_fontsize=fontsize * 1.0, + cbar_tick_fontsize=fontsize, + box_as_square=True, + vrange=var_range, + figsize=figsize, + cmap=cmap_color, + cmap_bounds=cmap_bounds, + cbar_kw={"extend": "both", "shrink": shrink}, + missing_color="white", + legend_on=legend_on, + legend_labels=legend_labels, + legend_box_xy=legend_box_xy, + legend_box_size=legend_box_size, + legend_lw=legend_lw, + legend_fontsize=legend_fontsize, + logo_rect=[0, 0, 0, 0], + logo_off=True) + + ax.axvline(x=len(model_list)-len(highlight_models),color="k",linewidth=3) + ax.set_xticklabels(model_list,rotation=45,va="bottom",ha="left") + ax.set_yticklabels(var_list,rotation=0,va="center",ha="right") + for xtick,color in zip(ax.get_xticklabels(),lable_colors): + xtick.set_color(color) + ax.yaxis.label.set_color(lable_colors[0]) + + # Save figure as an image file + outdir = os.path.join(out_path,region) + if not os.path.exists(outdir): + os.makedirs(outdir) + outfile = "{}_{}_portrait_{}.png".format(stat,region,group) + fig.savefig(os.path.join(outdir,outfile),facecolor="w", bbox_inches="tight") + plt.close(fig) + + return + +def collect_clim_diags(regions,variables,fig_format,model_name,case_id,input_dir,output_dir): + diag_metric = "mean_climate" + seasons = ['DJF','MAM','JJA','SON','AC'] + input_dir = input_dir.replace("%(metric_type)",diag_metric) + + #group figures + fig_sets = OrderedDict() + fig_sets['CLIM_patttern'] = ['graphics','*'] + for fset in fig_sets.keys(): + for region in regions: + for season in seasons: + for var in variables: + indir = input_dir.replace('%(output_type)',fig_sets[fset][0]) + fpaths = sorted(glob.glob(os.path.join(indir,var, + '{}{}_{}*.{}'.format(fig_sets[fset][1],region,season,fig_format)))) + for fpath in fpaths: + refname = fpath.split("/")[-1].split("_") + filname = '{}_{}_{}.{}'.format(refname[0],region,season,fig_format) + outpath = os.path.join( + output_dir.replace("%(group_type)",fset), + region,season) + if not os.path.exists(outpath): + os.makedirs(outpath) + outfile = os.path.join(outpath,filname) + os.rename(fpath,outfile) + + #orgnize metrics jason file + inpath = input_dir.replace('%(output_type)','metrics_results') + outpath = output_dir.replace('%(group_type)','metrics_data') + if not os.path.exists(outpath): + os.makedirs(outpath) + fpaths = sorted(glob.glob(os.path.join(inpath,'*.json'))) + for fpath in fpaths: + refname = fpath.split("/")[-1].split("_") + filname = '{}.{}.{}.{}.json'.format( + refname[0],refname[1],model_name,case_id, + ) + outfile = os.path.join(outpath,filname) + os.rename(fpath,outfile) + return + +def collect_movs_diags(modes,fig_format,model_name,case_id,input_dir,output_dir): + diag_metric = "variability_modes" + seasons = ['DJF','MAM','JJA','SON','yearly','monthly'] + input_dir = input_dir.replace("%(metric_type)",diag_metric) + + #group figures + fig_sets = OrderedDict() + fig_sets['MOV_eoftest'] = ['diagnostic_results','EG_Spec*'] + fig_sets['MOV_compose'] = ['graphics','*compare_obs'] + fig_sets['MOV_telecon'] = ['graphics','*teleconnection'] + fig_sets['MOV_pattern'] = ['graphics','*'] + + for fset in fig_sets.keys(): + for mode in modes: + for season in seasons: + if fset == "MOV_eoftest": + indir = input_dir.replace('%(output_type)',fig_sets[fset][0]) + template = '{}_{}_{}*.{}'.format(fig_sets[fset][1],mode,season,fig_format) + else: + indir = input_dir.replace('%(output_type)',fig_sets[fset][0]) + template = '{}*_{}*_{}.{}'.format(mode,season,fig_sets[fset][1],fig_format) + fpaths = sorted(glob.glob(os.path.join(indir,mode,'*',template))) + for fpath in fpaths: + refname = fpath.split("/")[-2] + filname = fpath.split("/")[-1] + if "_cbf_" in filname: + outfile = '{}_{}_{}_cbf.{}'.format(fset,mode,season,fig_format) + elif "EOF1" in filname: + outfile = '{}_{}_{}_eof1.{}'.format(fset,mode,season,fig_format) + elif "EOF2" in filname: + outfile = '{}_{}_{}_eof2.{}'.format(fset,mode,season,fig_format) + elif "EOF3" in filname: + outfile = '{}_{}_{}_eof3.{}'.format(fset,mode,season,fig_format) + outpath = os.path.join( + output_dir.replace("%(group_type)","MOV_metric"), + fset,season) + if not os.path.exists(outpath): + os.makedirs(outpath) + os.rename(fpath,os.path.join(outpath,outfile)) + + #orgnize metrics jason file + inpath = input_dir.replace('%(output_type)','metrics_results') + outpath = output_dir.replace('%(group_type)','metrics_data') + if not os.path.exists(outpath): + os.makedirs(outpath) + fpaths = sorted(glob.glob(os.path.join(inpath,diag_metric,'*/*/*.json'))) + for fpath in fpaths: + refmode = fpath.split("/")[-3] + refname = fpath.split("/")[-2] + reffile = fpath.split("/")[-1] + filname = '{}.{}.{}.{}.json'.format( + refmode,refname,model_name,case_id, + ) + if 'diveDown' in reffile: + outfile = os.path.join(outpath,filname.replace(".json","diveDown.json")) + else: + outfile = os.path.join(outpath,filname) + os.rename(fpath,outfile) + return + +def collect_enso_diags(groups,fig_format,refname,model_name,case_id,input_dir,output_dir): + diag_metric = "enso_metric" + input_dir = input_dir.replace("%(metric_type)",diag_metric) + + #group figures + fig_sets = OrderedDict() + fig_sets['ENSO_metric'] = ['graphics','*'] + for fset in fig_sets.keys(): + for group in groups: + fdir = input_dir.replace('%(output_type)',fig_sets[fset][0] ) + fpaths = sorted(glob.glob(os.path.join(fdir,group, + '{}.{}'.format(fig_sets[fset][1],fig_format)))) + for fpath in fpaths: + filname = fpath.split("/")[-1].split("_") + outpath = os.path.join(output_dir.replace("%(group_type)",fset),group) + if not os.path.exists(outpath): + os.makedirs(outpath) + outfile = '{}_{}_{}_{}'.format(group,filname[-3],filname[-2],filname[-1]) + os.rename(fpath,os.path.join(outpath,outfile)) + + #orgnize metrics jason file + inpath = input_dir.replace('%(output_type)','metrics_results') + outpath = output_dir.replace('%(group_type)','metrics_data') + if not os.path.exists(outpath): + os.makedirs(outpath) + fpaths = sorted(glob.glob(os.path.join(inpath,diag_metric,'*/*.json'))) + for fpath in fpaths: + refmode = fpath.split("/")[-2] + reffile = fpath.split("/")[-1] + filname = '{}.{}.{}.{}.json'.format( + refmode,refname,model_name,case_id, + ) + if 'diveDown' in reffile: + outfile = os.path.join(outpath,filname.replace(".json","diveDown.json")) + else: + outfile = os.path.join(outpath,filname) + os.rename(fpath,outfile) + return + diff --git a/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_atm_parameter_file.py b/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_atm_parameter_file.py new file mode 100755 index 000000000..b962d6143 --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_atm_parameter_file.py @@ -0,0 +1,131 @@ +import os +import sys +import json + +##################### +#basic information +##################### +model_name = 'e3sm.historical.v3-LR.0051' +start_yr = int('1900') +end_yr = int('2014') +num_years = end_yr - start_yr + 1 +period = "{:04d}{:02d}-{:04d}{:02d}".format(start_yr,1,end_yr,12) + +mip = model_name.split(".")[0] +exp = model_name.split(".")[1] +product = model_name.split(".")[2] +realm = model_name.split(".")[3] +tableId = 'Amon' +case_id = 'v20250131' + +seasons = 'DJF,MAM,JJA,SON,yearly,monthly'.split(",") +frequency = 'mo' + +#model +varModel = 'psl' +#unit conversion (namelist) +ModUnitsAdjust = (True,"divide",100.0) +test_data_path = 'ts' +test_data_set = [exp] + +#reference +varOBS = 'psl' +#unit conversion (namelist) +ObsUnitsAdjust = (True,"divide",100.0) + +obs_file = "ts_ref_variability_modes_atm_catalogue.json" +obs_dic = json.load(open(obs_file)) +reference_data_path = 'ts_ref' +reference_data_set = obs_dic[varOBS]['set'] +reference_data_name = obs_dic[varOBS][refset] +reference_data_path = obs_dic[varOBS][refname]['file_path'] +osyear = int(str(obs_dic[varOBS][refname]['yymms'])[0:4]) +oeyear = int(str(obs_dic[varOBS][refname]['yymme'])[0:4]) + +variability_mode = "NAM" #NAO,PNA,NPO,SAM,PSA1,PSA2 +eofn_mod = "1" #"1,1,2,1,2,3" +eofn_obs = "1" #"1,1,2,1,2,3" + +############################################## +#Configuration shared with pcmdi diagnostics +############################################## +# Record NetCDF output +nc_out_obs = True +nc_out_model = True +if nc_out_model or nc_out_obs: + ext = ".nc" +else: + ext = ".xml" +user_notes = 'Provenance and results' +debug = False + +# Generate plots +plot_model = True +plot_obs = True # optional + +# Additional settings +run_type = 'model_vs_obs' +figure_format = 'png' + +# Save interpolated model climatology ? +save_test_clims = True + +# Save Metrics Results in Single File ? +# option: 'y' or 'n', set to 'n' as we +# run pcmdi for each variable separately +metrics_in_single_file = 'n' + +# customize land/sea mask values +regions_values = {"land":100.,"ocean":0.} + +#setup template for land/sea mask (fixed) +modpath_lf = os.path.join( + 'fixed', + 'sftlf.%(model).nc' +) + +# If True, maskout land region thus consider only over ocean +landmask = False + +#template for model file +modnames = [ product ] +realization = "*" +modpath = os.path.join( + 'ts', + '{}.{}.%(model).%(realization).{}.%(variable).{}.nc'.format(mip,exp,tableId,period) +) + +#start and end year for analysis +msyear = int(start_yr) +meyear = int(end_yr) + +# If True, remove Domain Mean of each time step +RmDomainMean = True + +# If True, consider EOF with unit variance +EofScaling = False + +# Conduct CBF analysis +CBF = True + +# Conduct conventional EOF analysis +ConvEOF = True + +# Generate CMEC compliant json +cmec = False + +# Update diagnostic file if exist +update_json = False + +#results directory structure. +results_dir = os.path.join( + 'pcmdi_diags', + '%(output_type)', + 'variability_modes', + '%(mip)', + '%(exp)', + case_id, + '%(variability_mode)', + '%(reference_data_name)', +) + diff --git a/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_cpl_parameter_file.py b/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_cpl_parameter_file.py new file mode 100755 index 000000000..f6eb4503a --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/e3sm_mov_cpl_parameter_file.py @@ -0,0 +1,130 @@ +import os +import sys +import json + +##################### +#basic information +##################### +model_name = 'e3sm.historical.v3-LR.0051' +start_yr = int('1900') +end_yr = int('2014') +num_years = end_yr - start_yr + 1 +period = "{:04d}{:02d}-{:04d}{:02d}".format(start_yr,1,end_yr,12) + +mip = model_name.split(".")[0] +exp = model_name.split(".")[1] +product = model_name.split(".")[2] +realm = model_name.split(".")[3] +tableId = 'Amon' +case_id = 'v20250131' + +seasons = 'yearly,monthly'.split(",") +frequency = 'mo' + +#model +varModel = 'ts' +#unit conversion (namelist) +ModUnitsAdjust = (True,"subtract",273.15) +test_data_path = 'ts' +test_data_set = [exp] + +#reference +varOBS = 'ts' +#unit conversion (namelist) +ObsUnitsAdjust = (True,"subtract",273.15) +obs_file = "ts_ref_variability_modes_cpl_catalogue.json" +obs_dic = json.load(open(obs_file)) +reference_data_path = 'ts_ref' +reference_data_set = obs_dic[varOBS]['set'] +reference_data_name = obs_dic[varOBS][refset] +reference_data_path = obs_dic[varOBS][refname]['file_path'] +osyear = int(str(obs_dic[varOBS][refname]['yymms'])[0:4]) +oeyear = int(str(obs_dic[varOBS][refname]['yymme'])[0:4]) + +variability_mode = "PDO" #NPGO,AMO +eofn_mod = "1" #"2,2" +eofn_obs = "1" #"2,2" + +############################################## +#Configuration shared with pcmdi diagnostics +############################################## +# Record NetCDF output +nc_out_obs = True +nc_out_model = True +if nc_out_model or nc_out_obs: + ext = ".nc" +else: + ext = ".xml" +user_notes = 'Provenance and results' +debug = False + +# Generate plots +plot_model = True +plot_obs = True # optional + +# Additional settings +run_type = 'model_vs_obs' +figure_format = 'png' + +# Save interpolated model climatology ? +save_test_clims = True + +# Save Metrics Results in Single File ? +# option: 'y' or 'n', set to 'n' as we +# run pcmdi for each variable separately +metrics_in_single_file = 'n' + +# customize land/sea mask values +regions_values = {"land":100.,"ocean":0.} + +#setup template for land/sea mask (fixed) +modpath_lf = os.path.join( + 'fixed', + 'sftlf.%(model).nc' +) + +# If True, maskout land region thus consider only over ocean +landmask = True + +#template for model file +modnames = [ product ] +realization = "*" +modpath = os.path.join( + 'ts', + '{}.{}.%(model).%(realization).{}.%(variable).{}.nc'.format(mip,exp,tableId,period) +) + +#start and end year for analysis +msyear = int(start_yr) +meyear = int(end_yr) + +# If True, remove Domain Mean of each time step +RmDomainMean = True + +# If True, consider EOF with unit variance +EofScaling = False + +# Conduct CBF analysis +CBF = True + +# Conduct conventional EOF analysis +ConvEOF = True + +# Generate CMEC compliant json +cmec = False + +# Update diagnostic file if exist +update_json = False + +#results directory structure. +results_dir = os.path.join( + 'pcmdi_diags', + '%(output_type)', + 'variability_modes', + '%(mip)', + '%(exp)', + case_id, + '%(variability_mode)', + '%(reference_data_name)', +) + diff --git a/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_atm_catalogue.json b/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_atm_catalogue.json new file mode 100755 index 000000000..17ff5cb4a --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_atm_catalogue.json @@ -0,0 +1,19 @@ +{ + "psl": { + "set": "alternate2", + "alternate2": "NOAA-20C", + "NOAA-20C": { + "mip": "obs", + "exp": "historical", + "model": "NOAA-20C", + "realization": "00", + "tableID": "Amon", + "yymms": "190001", + "yymme": "201412", + "var_in_file": "psl", + "var_name": "psl", + "file_path": "ts_ref/obs.historical.NOAA-20C.00.Amon.psl.190001-201412.nc", + "template": "obs.historical.NOAA-20C.00.Amon.psl.190001-201412.nc" + } + } +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_cpl_catalogue.json b/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_cpl_catalogue.json new file mode 100755 index 000000000..0b5790e45 --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/ts_ref_variability_modes_cpl_catalogue.json @@ -0,0 +1,19 @@ +{ + "ts": { + "set": "alternate1", + "alternate1": "HadISST2", + "HadISST2": { + "mip": "obs", + "exp": "historical", + "model": "HadISST2", + "realization": "00", + "tableID": "Amon", + "yymms": "190001", + "yymme": "201412", + "var_in_file": "ts", + "var_name": "ts", + "file_path": "ts_ref/obs.historical.HadISST2.00.Amon.ts.190001-201412.nc", + "template": "obs.historical.HadISST2.00.Amon.ts.190001-201412.nc" + } + } +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_atm_catalogue.json b/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_atm_catalogue.json new file mode 100755 index 000000000..a4508f9f7 --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_atm_catalogue.json @@ -0,0 +1,19 @@ +{ + "psl": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "190001", + "yymme": "201412", + "var_in_file": "psl", + "var_name": "psl", + "file_path": "ts/e3sm.historical.v3-LR.0051.Amon.psl.190001-201412.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.psl.190001-201412.nc" + } + } +} \ No newline at end of file diff --git a/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_cpl_catalogue.json b/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_cpl_catalogue.json new file mode 100755 index 000000000..9a3d9f559 --- /dev/null +++ b/sample_setups/external-setups/E3SM/varibility_modes/ts_variability_modes_cpl_catalogue.json @@ -0,0 +1,19 @@ +{ + "ts": { + "set": "historical", + "historical": "v3-LR", + "v3-LR": { + "mip": "e3sm", + "exp": "historical", + "model": "v3-LR", + "realization": "0051", + "tableID": "Amon", + "yymms": "190001", + "yymme": "201412", + "var_in_file": "ts", + "var_name": "ts", + "file_path": "ts/e3sm.historical.v3-LR.0051.Amon.ts.190001-201412.nc", + "template": "e3sm.historical.v3-LR.0051.Amon.ts.190001-201412.nc" + } + } +} \ No newline at end of file From d02ede9a64b9fb490936f0f7873957f21dbe9d31 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 10:34:20 -0800 Subject: [PATCH 02/13] Create database.py --- pcmdi_metrics/utils/database.py | 413 ++++++++++++++++++++++++++++++++ 1 file changed, 413 insertions(+) create mode 100644 pcmdi_metrics/utils/database.py diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py new file mode 100644 index 000000000..7f304b02b --- /dev/null +++ b/pcmdi_metrics/utils/database.py @@ -0,0 +1,413 @@ +import os +import re +import requests +import json + +def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool=False): + """ + Retrieves JSON files from the PMP Archive based on specified mip, model, exp, and metrics. + + Parameters + ---------- + mip : str + The model intercomparison project (e.g., 'cmip5', 'cmip6'). + model : str + The model name (e.g., 'ACCESS-CM2', 'EC-EARTH'). + exp : str + The experiment (e.g., 'historical', 'amip'). + metrics : list, optional + List of metrics (e.g., 'enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'). Default None retrieves files for all metrics. + debug : bool, optional + If true, print more interim outputs for debugging. + Returns + ------- + dict + A dictionary of JSON files from the PMP Archive database. + """ + + if metrics is None: + metrics = ['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'] + + subdir_dict = load_subdir_dict() + results_dict = dict() + + for metric in metrics: + + json_url_list = find_pmp_archive_json_urls(metric, mip, exp) + subdirs = subdir_dict.get(metric, {}).get(mip, {}).get(exp, ".") + + if debug: + print(metric, json_url_list, subdirs) + print(len(json_url_list), len(subdirs)) + print("metric, json_url_list, subdirs:", metric, json_url_list, subdirs) + + results_dict[metric] = dict() + + keys = list() + + for i, url in enumerate(json_url_list): + tmp_dict = load_json_from_url(url) + + # Initialize a dict + results_dict_i = dict() + results_dict_i["RESULTS"] = dict() + results_dict_i["RESULTS"][model] = None + + # Find available models + if "RESULTS" in tmp_dict.keys(): + if metric == "enso_metric": + models = tmp_dict["RESULTS"]["model"].keys() + else: + models = tmp_dict["RESULTS"].keys() + models = sorted(list(models)) + + if debug: + print(metric, tmp_dict["RESULTS"].keys()) + print("models:", models) + + # Find info for the model + if model is not None: + if model in models: + if metric == "enso_metric": + results_dict_i["RESULTS"][model] = tmp_dict["RESULTS"]["model"][model] + else: + results_dict_i["RESULTS"][model] = tmp_dict["RESULTS"][model] + else: + results_dict_i["RESULTS"] = tmp_dict["RESULTS"] + + # Find provenance info + if "provenance" in tmp_dict.keys(): + results_dict_i["provenance"] = tmp_dict["provenance"] + + potential_keys_for_reference = ["REFERENCE", "reference", "Reference", "References", "REF"] + + # Find reference info + for potential_key in potential_keys_for_reference: + if potential_key in tmp_dict.keys(): + results_dict_i["REFERENCE"] = tmp_dict[potential_key] + break + else: + results_dict_i["REFERENCE"] = None + + # Name the key + key = os.path.basename(url).replace(".json", "") + + # Update the key name if following condition is met + if len(json_url_list) == len(subdirs): + key = subdirs[i] + elif "Variable" in tmp_dict.keys(): + key = tmp_dict["Variable"]["id"] + else: + if metric == "mean_climate": + if "variable_id" in list(tmp_dict.keys()): + key = tmp_dict["variable_id"] + keys.append(key) + + # Add the content to results dict + if key == ".": + results_dict[metric] = results_dict_i + else: + results_dict[metric][key] = results_dict_i + + # Find sub keys, just to check + if debug: + sub_keys = list(tmp_dict.keys()) + print("metric, key, sub_keys:", metric, key, sub_keys) + + if debug: + print("metric, keys:", metric, keys, '\n') + + print(f"Found {len(json_url_list)} JSON files for metric '{metric}' and collected info for model '{model}'.") + + return results_dict + +def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, search_keys:list=None): + """ + Find PMP archive JSON URLs based on the provided metric, mip, exp, and optional version and search keys. + + Parameters + ---------- + metric : str + The metric to search for (e.g., 'enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'). + mip : str + The model intercomparison project (e.g., 'cmip5', 'cmip6'). + exp : str + The experiment (e.g., 'historical', 'amip'). + version : str, optional + The version of the dataset, by default None. + search_keys : list, optional + A list of search keys to filter the URLs, by default None. + + Returns + ------- + list + A list of URLs matching the search criteria. + """ + version_dict = load_version_dict() + subdir_dict = load_subdir_dict() + + github_repo = "https://github.com/PCMDI/pcmdi_metrics_results_archive" + branch = "tree/main" + + if version is None: + try: + version = version_dict[metric][mip][exp] + except KeyError: + raise KeyError(f"Version not found for metric '{metric}', mip '{mip}', and experiment '{exp}'.") + + # List of available metrics + # Available options for metrics: enso_metric, mean_climate, variability_modes, mjo, qbo-mjo + # will add precip and possibly others later ... + available_metrics = list(version_dict.keys()) + + subdirs = subdir_dict.get(metric, {}).get(mip, {}).get(exp, ".") + + urls_interim = list() + urls_final = list() + + if metric not in available_metrics: + raise ValueError(f"Metric '{metric}' is not supported.") + + for subdir in subdirs: + dir_url = os.path.join(github_repo, branch, "metrics_results", metric, mip, exp, version, subdir) + urls = find_json_files_in_the_directory(dir_url) + urls_interim.extend(urls) + + if search_keys is not None: + for url in urls_interim: + for search_key in search_keys: + if search_key in url: + urls_final.append(url) + else: + urls_final = urls_interim + + return urls_final + +def load_version_dict(): + """ + Create a dictionary of each metric and mip's corresponding data version. + + Returns + ------- + dict + A dictionary of relevant data versions for each metric and mip. + """ + version_dict = { + "enso_metric":{ + "cmip5":{ + "historical": "v20210104" + }, + "cmip6":{ + "historical": "v20210620" + } + }, + "mean_climate":{ + "cmip5":{ + "amip": "v20200429", + "historical": "v20220928" + }, + "cmip6":{ + "amip": "v20210830", + "historical": "v20230823" + } + }, + "variability_modes":{ + "cmip3":{ + "20c3m": "v20210119", + "amip": "v20210119" + }, + "cmip5":{ + "amip": "v20210119", + "historical": "v20210119" + }, + "cmip6":{ + "amip": "v20210119", + "historical": "v20220825" + } + }, + "mjo":{ + "cmip5":{ + "historical": "v20230924" + }, + "cmip6":{ + "historical": "v20230924" + } + }, + "qbo-mjo":{ + "cmip5":{ + "historical": "v20240422" + }, + "cmip6":{ + "historical": "v20240422" + } + } + } + return version_dict + + +def load_subdir_dict(): + """ + Create a dictionary of each metric and mip's corresponding subdirectories. + + Returns + ------- + dict + A dictionary of each metric and mip subdirectory structure. + """ + subdir_dict = { + "enso_metric":{ + "cmip5":{ + "historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"] + }, + "cmip6":{ + "historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"] + } + }, + "mean_climate":{ + "cmip5":{ + "amip": ["."], + "historical": ["."] + }, + "cmip6":{ + "amip": ["."], + "historical": ["."] + } + }, + "variability_modes":{ + "cmip3":{ + "20c3m": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PDO/HadISSTv1.1", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + "amip": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + }, + "cmip5":{ + "amip": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + "historical": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PDO/HadISSTv1.1", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + }, + "cmip6":{ + "amip": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + "historical": [ + "NAM/NOAA-CIRES_20CR", + "NAO/NOAA-CIRES_20CR", + "NPGO/HadISSTv1.1", + "NPO/NOAA-CIRES_20CR", + "PDO/HadISSTv1.1", + "PNA/NOAA-CIRES_20CR", + "SAM/NOAA-CIRES_20CR" + ], + } + }, + } + return subdir_dict + +def find_json_files_in_the_directory(url): + """ + Find JSON file URLs based on directory URL. + + Parameters + ------- + url : str + The URL of the directory where JSON files are stored (e.g., pcmdi_metrics_results_archive). + + Returns + ------- + list + A list of all URLs for available JSON files in the given directory. + """ + response = requests.get(url) + if response.status_code != 200: + raise Exception( + f"Failed to fetch URL: {url} (Status Code: {response.status_code})" + ) + + # Extract file links using a regex pattern + html_content = response.text + file_pattern = re.compile(r'href="(/[^/]+/[^/]+/blob/[^"]+)"') + matches = file_pattern.findall(html_content) + urls = list() + + # GitHub raw content base URL + base_raw_url = "https://raw.githubusercontent.com" + + if matches: + # Remove duplicates and download each file + matches = list(set(matches)) + for match in matches: + raw_file_url = base_raw_url + match.replace("/blob/", "/") + urls.append(raw_file_url) + + return urls + +def load_json_from_url(url): + """ + Load JSON data from a given URL. + + Parameters + ---------- + url : str + The URL of the JSON file to be loaded. + + Returns + ------- + dict or None + The JSON data as a dictionary if the request is successful and the content is valid JSON. + Returns None if there is an error fetching the JSON file or decoding the JSON content. + + Example + ------- + >>> url = 'https://example.com/path/to/your/file.json' # Replace with your JSON file URL + >>> json_data = load_json_from_url(url) + """ + + try: + # Send a GET request to the URL + response = requests.get(url) + + # Check if the request was successful + response.raise_for_status() + + # Load the response content as JSON + data = response.json() + + return data + except requests.exceptions.RequestException as e: + print(f"Error fetching the JSON file: {e}") + return None + except json.JSONDecodeError: + print("Error decoding JSON") + return None \ No newline at end of file From c6daa15ce408d81d937745ebb7d606ebe61162f8 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 10:46:01 -0800 Subject: [PATCH 03/13] Update __init__.py --- pcmdi_metrics/utils/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/pcmdi_metrics/utils/__init__.py b/pcmdi_metrics/utils/__init__.py index f47d51950..5a9f81176 100644 --- a/pcmdi_metrics/utils/__init__.py +++ b/pcmdi_metrics/utils/__init__.py @@ -28,3 +28,8 @@ from .sort_human import sort_human from .string_constructor import StringConstructor, fill_template from .tree_dict import tree +from .database import ( + database_metrics, + load_json_from_url, + find_pmp_archive_json_urls, +) \ No newline at end of file From 23ffbf950d34ed54ff834a792fcc4c3ca0b09b40 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 10:50:31 -0800 Subject: [PATCH 04/13] removed trailing whitespaces --- pcmdi_metrics/utils/database.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index 7f304b02b..a38bd70e4 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -28,7 +28,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= if metrics is None: metrics = ['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'] - subdir_dict = load_subdir_dict() + subdir_dict = load_subdir_dict() results_dict = dict() for metric in metrics: @@ -61,7 +61,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= models = tmp_dict["RESULTS"].keys() models = sorted(list(models)) - if debug: + if debug: print(metric, tmp_dict["RESULTS"].keys()) print("models:", models) @@ -75,7 +75,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= else: results_dict_i["RESULTS"] = tmp_dict["RESULTS"] - # Find provenance info + # Find provenance info if "provenance" in tmp_dict.keys(): results_dict_i["provenance"] = tmp_dict["provenance"] @@ -105,7 +105,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= # Add the content to results dict if key == ".": - results_dict[metric] = results_dict_i + results_dict[metric] = results_dict_i else: results_dict[metric][key] = results_dict_i From 48c28d5dd371ce452bb93f7041f8f69125621b03 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 10:55:57 -0800 Subject: [PATCH 05/13] Update database.py --- pcmdi_metrics/utils/database.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index a38bd70e4..ba19ed7cf 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -3,6 +3,7 @@ import requests import json + def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool=False): """ Retrieves JSON files from the PMP Archive based on specified mip, model, exp, and metrics. @@ -121,6 +122,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= return results_dict + def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, search_keys:list=None): """ Find PMP archive JSON URLs based on the provided metric, mip, exp, and optional version and search keys. @@ -183,6 +185,7 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s return urls_final + def load_version_dict(): """ Create a dictionary of each metric and mip's corresponding data version. @@ -335,6 +338,7 @@ def load_subdir_dict(): } return subdir_dict + def find_json_files_in_the_directory(url): """ Find JSON file URLs based on directory URL. @@ -373,6 +377,7 @@ def find_json_files_in_the_directory(url): return urls + def load_json_from_url(url): """ Load JSON data from a given URL. @@ -410,4 +415,5 @@ def load_json_from_url(url): return None except json.JSONDecodeError: print("Error decoding JSON") - return None \ No newline at end of file + return None + \ No newline at end of file From de45dc1e72df13670373899ac9eea80f82005c0b Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:02:25 -0800 Subject: [PATCH 06/13] Update database.py --- pcmdi_metrics/utils/database.py | 61 ++++++++++++++++----------------- 1 file changed, 30 insertions(+), 31 deletions(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index ba19ed7cf..3c53b463f 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -25,35 +25,35 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= dict A dictionary of JSON files from the PMP Archive database. """ - + if metrics is None: metrics = ['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'] subdir_dict = load_subdir_dict() results_dict = dict() - + for metric in metrics: - + json_url_list = find_pmp_archive_json_urls(metric, mip, exp) subdirs = subdir_dict.get(metric, {}).get(mip, {}).get(exp, ".") - + if debug: print(metric, json_url_list, subdirs) print(len(json_url_list), len(subdirs)) print("metric, json_url_list, subdirs:", metric, json_url_list, subdirs) - + results_dict[metric] = dict() - + keys = list() - + for i, url in enumerate(json_url_list): tmp_dict = load_json_from_url(url) - + # Initialize a dict results_dict_i = dict() results_dict_i["RESULTS"] = dict() results_dict_i["RESULTS"][model] = None - + # Find available models if "RESULTS" in tmp_dict.keys(): if metric == "enso_metric": @@ -61,7 +61,7 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= else: models = tmp_dict["RESULTS"].keys() models = sorted(list(models)) - + if debug: print(metric, tmp_dict["RESULTS"].keys()) print("models:", models) @@ -79,9 +79,9 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= # Find provenance info if "provenance" in tmp_dict.keys(): results_dict_i["provenance"] = tmp_dict["provenance"] - + potential_keys_for_reference = ["REFERENCE", "reference", "Reference", "References", "REF"] - + # Find reference info for potential_key in potential_keys_for_reference: if potential_key in tmp_dict.keys(): @@ -89,10 +89,10 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= break else: results_dict_i["REFERENCE"] = None - + # Name the key key = os.path.basename(url).replace(".json", "") - + # Update the key name if following condition is met if len(json_url_list) == len(subdirs): key = subdirs[i] @@ -114,12 +114,12 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= if debug: sub_keys = list(tmp_dict.keys()) print("metric, key, sub_keys:", metric, key, sub_keys) - + if debug: print("metric, keys:", metric, keys, '\n') - + print(f"Found {len(json_url_list)} JSON files for metric '{metric}' and collected info for model '{model}'.") - + return results_dict @@ -147,7 +147,7 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s """ version_dict = load_version_dict() subdir_dict = load_subdir_dict() - + github_repo = "https://github.com/PCMDI/pcmdi_metrics_results_archive" branch = "tree/main" @@ -161,12 +161,12 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s # Available options for metrics: enso_metric, mean_climate, variability_modes, mjo, qbo-mjo # will add precip and possibly others later ... available_metrics = list(version_dict.keys()) - + subdirs = subdir_dict.get(metric, {}).get(mip, {}).get(exp, ".") - + urls_interim = list() urls_final = list() - + if metric not in available_metrics: raise ValueError(f"Metric '{metric}' is not supported.") @@ -174,7 +174,7 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s dir_url = os.path.join(github_repo, branch, "metrics_results", metric, mip, exp, version, subdir) urls = find_json_files_in_the_directory(dir_url) urls_interim.extend(urls) - + if search_keys is not None: for url in urls_interim: for search_key in search_keys: @@ -182,7 +182,7 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s urls_final.append(url) else: urls_final = urls_interim - + return urls_final @@ -244,7 +244,7 @@ def load_version_dict(): "historical": "v20240422" } } - } + } return version_dict @@ -333,7 +333,7 @@ def load_subdir_dict(): "PNA/NOAA-CIRES_20CR", "SAM/NOAA-CIRES_20CR" ], - } + } }, } return subdir_dict @@ -374,7 +374,7 @@ def find_json_files_in_the_directory(url): for match in matches: raw_file_url = base_raw_url + match.replace("/blob/", "/") urls.append(raw_file_url) - + return urls @@ -398,17 +398,17 @@ def load_json_from_url(url): >>> url = 'https://example.com/path/to/your/file.json' # Replace with your JSON file URL >>> json_data = load_json_from_url(url) """ - + try: # Send a GET request to the URL response = requests.get(url) - + # Check if the request was successful response.raise_for_status() - + # Load the response content as JSON data = response.json() - + return data except requests.exceptions.RequestException as e: print(f"Error fetching the JSON file: {e}") @@ -416,4 +416,3 @@ def load_json_from_url(url): except json.JSONDecodeError: print("Error decoding JSON") return None - \ No newline at end of file From 331ade88b0f9fc9e8ab71492535fb648d013b031 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:05:24 -0800 Subject: [PATCH 07/13] Update database.py --- pcmdi_metrics/utils/database.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index 3c53b463f..da4a69675 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -212,7 +212,7 @@ def load_version_dict(): "cmip6":{ "amip": "v20210830", "historical": "v20230823" - } + } }, "variability_modes":{ "cmip3":{ @@ -226,7 +226,7 @@ def load_version_dict(): "cmip6":{ "amip": "v20210119", "historical": "v20220825" - } + } }, "mjo":{ "cmip5":{ @@ -274,7 +274,7 @@ def load_subdir_dict(): "cmip6":{ "amip": ["."], "historical": ["."] - } + } }, "variability_modes":{ "cmip3":{ @@ -381,7 +381,7 @@ def find_json_files_in_the_directory(url): def load_json_from_url(url): """ Load JSON data from a given URL. - + Parameters ---------- url : str From 03bb526de65346a8cba006779871e3bc97d569ad Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:06:42 -0800 Subject: [PATCH 08/13] Update database.py --- pcmdi_metrics/utils/database.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index da4a69675..18b9e2b69 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -381,7 +381,7 @@ def find_json_files_in_the_directory(url): def load_json_from_url(url): """ Load JSON data from a given URL. - + Parameters ---------- url : str From 4897a6b90a15f94661f4b06fd7ded4773ccb3fef Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:08:14 -0800 Subject: [PATCH 09/13] Update __init__.py --- pcmdi_metrics/utils/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcmdi_metrics/utils/__init__.py b/pcmdi_metrics/utils/__init__.py index 5a9f81176..1489caf9c 100644 --- a/pcmdi_metrics/utils/__init__.py +++ b/pcmdi_metrics/utils/__init__.py @@ -32,4 +32,4 @@ database_metrics, load_json_from_url, find_pmp_archive_json_urls, -) \ No newline at end of file +) From bd569115cea2b7d33cf9a9db44271432b168ec50 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:27:22 -0800 Subject: [PATCH 10/13] pre-commit check done --- pcmdi_metrics/utils/database.py | 145 ++++++++++++++------------------ 1 file changed, 63 insertions(+), 82 deletions(-) diff --git a/pcmdi_metrics/utils/database.py b/pcmdi_metrics/utils/database.py index 18b9e2b69..762d31bca 100644 --- a/pcmdi_metrics/utils/database.py +++ b/pcmdi_metrics/utils/database.py @@ -1,10 +1,13 @@ +import json import os import re + import requests -import json -def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool=False): +def database_metrics( + mip: str, model: str, exp: str, metrics: list = None, debug: bool = False +): """ Retrieves JSON files from the PMP Archive based on specified mip, model, exp, and metrics. @@ -27,13 +30,12 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= """ if metrics is None: - metrics = ['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo'] + metrics = ["enso_metric", "mean_climate", "mjo", "variability_modes", "qbo-mjo"] subdir_dict = load_subdir_dict() results_dict = dict() for metric in metrics: - json_url_list = find_pmp_archive_json_urls(metric, mip, exp) subdirs = subdir_dict.get(metric, {}).get(mip, {}).get(exp, ".") @@ -70,7 +72,9 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= if model is not None: if model in models: if metric == "enso_metric": - results_dict_i["RESULTS"][model] = tmp_dict["RESULTS"]["model"][model] + results_dict_i["RESULTS"][model] = tmp_dict["RESULTS"]["model"][ + model + ] else: results_dict_i["RESULTS"][model] = tmp_dict["RESULTS"][model] else: @@ -80,7 +84,13 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= if "provenance" in tmp_dict.keys(): results_dict_i["provenance"] = tmp_dict["provenance"] - potential_keys_for_reference = ["REFERENCE", "reference", "Reference", "References", "REF"] + potential_keys_for_reference = [ + "REFERENCE", + "reference", + "Reference", + "References", + "REF", + ] # Find reference info for potential_key in potential_keys_for_reference: @@ -116,14 +126,18 @@ def database_metrics(mip:str, model:str, exp:str, metrics:list=None, debug:bool= print("metric, key, sub_keys:", metric, key, sub_keys) if debug: - print("metric, keys:", metric, keys, '\n') + print("metric, keys:", metric, keys, "\n") - print(f"Found {len(json_url_list)} JSON files for metric '{metric}' and collected info for model '{model}'.") + print( + f"Found {len(json_url_list)} JSON files for metric '{metric}' and collected info for model '{model}'." + ) return results_dict -def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, search_keys:list=None): +def find_pmp_archive_json_urls( + metric: str, mip: str, exp: str, version: str = None, search_keys: list = None +): """ Find PMP archive JSON URLs based on the provided metric, mip, exp, and optional version and search keys. @@ -155,7 +169,9 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s try: version = version_dict[metric][mip][exp] except KeyError: - raise KeyError(f"Version not found for metric '{metric}', mip '{mip}', and experiment '{exp}'.") + raise KeyError( + f"Version not found for metric '{metric}', mip '{mip}', and experiment '{exp}'." + ) # List of available metrics # Available options for metrics: enso_metric, mean_climate, variability_modes, mjo, qbo-mjo @@ -171,7 +187,9 @@ def find_pmp_archive_json_urls(metric:str, mip:str, exp:str, version:str=None, s raise ValueError(f"Metric '{metric}' is not supported.") for subdir in subdirs: - dir_url = os.path.join(github_repo, branch, "metrics_results", metric, mip, exp, version, subdir) + dir_url = os.path.join( + github_repo, branch, "metrics_results", metric, mip, exp, version, subdir + ) urls = find_json_files_in_the_directory(dir_url) urls_interim.extend(urls) @@ -196,54 +214,27 @@ def load_version_dict(): A dictionary of relevant data versions for each metric and mip. """ version_dict = { - "enso_metric":{ - "cmip5":{ - "historical": "v20210104" - }, - "cmip6":{ - "historical": "v20210620" - } + "enso_metric": { + "cmip5": {"historical": "v20210104"}, + "cmip6": {"historical": "v20210620"}, }, - "mean_climate":{ - "cmip5":{ - "amip": "v20200429", - "historical": "v20220928" - }, - "cmip6":{ - "amip": "v20210830", - "historical": "v20230823" - } + "mean_climate": { + "cmip5": {"amip": "v20200429", "historical": "v20220928"}, + "cmip6": {"amip": "v20210830", "historical": "v20230823"}, }, - "variability_modes":{ - "cmip3":{ - "20c3m": "v20210119", - "amip": "v20210119" - }, - "cmip5":{ - "amip": "v20210119", - "historical": "v20210119" - }, - "cmip6":{ - "amip": "v20210119", - "historical": "v20220825" - } + "variability_modes": { + "cmip3": {"20c3m": "v20210119", "amip": "v20210119"}, + "cmip5": {"amip": "v20210119", "historical": "v20210119"}, + "cmip6": {"amip": "v20210119", "historical": "v20220825"}, }, - "mjo":{ - "cmip5":{ - "historical": "v20230924" - }, - "cmip6":{ - "historical": "v20230924" - } + "mjo": { + "cmip5": {"historical": "v20230924"}, + "cmip6": {"historical": "v20230924"}, + }, + "qbo-mjo": { + "cmip5": {"historical": "v20240422"}, + "cmip6": {"historical": "v20240422"}, }, - "qbo-mjo":{ - "cmip5":{ - "historical": "v20240422" - }, - "cmip6":{ - "historical": "v20240422" - } - } } return version_dict @@ -258,26 +249,16 @@ def load_subdir_dict(): A dictionary of each metric and mip subdirectory structure. """ subdir_dict = { - "enso_metric":{ - "cmip5":{ - "historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"] - }, - "cmip6":{ - "historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"] - } + "enso_metric": { + "cmip5": {"historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"]}, + "cmip6": {"historical": ["ENSO_perf", "ENSO_proc", "ENSO_tel"]}, }, - "mean_climate":{ - "cmip5":{ - "amip": ["."], - "historical": ["."] - }, - "cmip6":{ - "amip": ["."], - "historical": ["."] - } + "mean_climate": { + "cmip5": {"amip": ["."], "historical": ["."]}, + "cmip6": {"amip": ["."], "historical": ["."]}, }, - "variability_modes":{ - "cmip3":{ + "variability_modes": { + "cmip3": { "20c3m": [ "NAM/NOAA-CIRES_20CR", "NAO/NOAA-CIRES_20CR", @@ -285,7 +266,7 @@ def load_subdir_dict(): "NPO/NOAA-CIRES_20CR", "PDO/HadISSTv1.1", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], "amip": [ "NAM/NOAA-CIRES_20CR", @@ -293,17 +274,17 @@ def load_subdir_dict(): "NPGO/HadISSTv1.1", "NPO/NOAA-CIRES_20CR", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], }, - "cmip5":{ + "cmip5": { "amip": [ "NAM/NOAA-CIRES_20CR", "NAO/NOAA-CIRES_20CR", "NPGO/HadISSTv1.1", "NPO/NOAA-CIRES_20CR", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], "historical": [ "NAM/NOAA-CIRES_20CR", @@ -312,17 +293,17 @@ def load_subdir_dict(): "NPO/NOAA-CIRES_20CR", "PDO/HadISSTv1.1", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], }, - "cmip6":{ + "cmip6": { "amip": [ "NAM/NOAA-CIRES_20CR", "NAO/NOAA-CIRES_20CR", "NPGO/HadISSTv1.1", "NPO/NOAA-CIRES_20CR", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], "historical": [ "NAM/NOAA-CIRES_20CR", @@ -331,9 +312,9 @@ def load_subdir_dict(): "NPO/NOAA-CIRES_20CR", "PDO/HadISSTv1.1", "PNA/NOAA-CIRES_20CR", - "SAM/NOAA-CIRES_20CR" + "SAM/NOAA-CIRES_20CR", ], - } + }, }, } return subdir_dict From 8bcc7eaa5dc546e08fbc87159bddda767f5d17a9 Mon Sep 17 00:00:00 2001 From: Kristin Chang Date: Thu, 6 Feb 2025 11:31:57 -0800 Subject: [PATCH 11/13] Update __init__.py --- pcmdi_metrics/utils/__init__.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/pcmdi_metrics/utils/__init__.py b/pcmdi_metrics/utils/__init__.py index 1489caf9c..94b3411ee 100644 --- a/pcmdi_metrics/utils/__init__.py +++ b/pcmdi_metrics/utils/__init__.py @@ -5,6 +5,7 @@ generate_calendar_months, subset_timesteps_in_custom_season, ) +from .database import database_metrics, find_pmp_archive_json_urls, load_json_from_url from .dates import ( date_to_str, extract_date_components, @@ -28,8 +29,3 @@ from .sort_human import sort_human from .string_constructor import StringConstructor, fill_template from .tree_dict import tree -from .database import ( - database_metrics, - load_json_from_url, - find_pmp_archive_json_urls, -) From d72664cfa8facbb96457ac664ac23043d5a768f6 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 13:53:36 -0800 Subject: [PATCH 12/13] initial draft --- docs/examples/pmp_metrics_database.ipynb | 166932 ++++++++++++++++++++ 1 file changed, 166932 insertions(+) create mode 100644 docs/examples/pmp_metrics_database.ipynb diff --git a/docs/examples/pmp_metrics_database.ipynb b/docs/examples/pmp_metrics_database.ipynb new file mode 100644 index 000000000..796efe9a2 --- /dev/null +++ b/docs/examples/pmp_metrics_database.ipynb @@ -0,0 +1,166932 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Access to online PMP output database\n", + "\n", + "This notebook provides example usages of PMP APIs to access the [PMP output online archive](https://github.com/PCMDI/pcmdi_metrics_results_archive) and load the information.\n", + "\n", + "Kristin Chang, Jiwoo Lee (LLNL)\n", + "\n", + "2025.02" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pcmdi_metrics.utils import database_metrics, find_pmp_archive_json_urls, load_json_from_url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find and load PMP output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage examples" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_tel/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"enso_metric\", \"cmip6\", \"historical\")\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"enso_metric\", \"cmip6\", \"historical\", search_keys=[\"ENSO_perf\"])\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/zg-500.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ua-200.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlds.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tauv.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsdt.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/sfcWind.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/pr.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlus.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsdscs.cmip6.historical.regrid2.2p5x2p5.v20230823.json']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"mean_climate\", \"cmip6\", \"historical\")\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tas.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/prw.cmip6.historical.regrid2.2p5x2p5.v20230823.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/pr.cmip6.historical.regrid2.2p5x2p5.v20230823.json']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"mean_climate\", \"cmip6\", \"historical\", search_keys=[\"tas\", \"pr\"])\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAM/NOAA-CIRES_20CR/var_mode_NAM_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAO/NOAA-CIRES_20CR/var_mode_NAO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NPGO/HadISSTv1.1/var_mode_NPGO_EOF2_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NPO/NOAA-CIRES_20CR/var_mode_NPO_EOF2_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PDO/HadISSTv1.1/var_mode_PDO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PNA/NOAA-CIRES_20CR/var_mode_PNA_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/SAM/NOAA-CIRES_20CR/var_mode_SAM_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"variability_modes\", \"cmip6\", \"historical\")\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAO/NOAA-CIRES_20CR/var_mode_NAO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',\n", + " 'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PDO/HadISSTv1.1/var_mode_PDO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"variability_modes\", \"cmip6\", \"historical\", search_keys=[\"NAO\", \"PDO\"])\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mjo/cmip6/historical/v20230924/mjo_stat_cmip6_historical_da_atm_allModels_allRuns_1985-2004.json']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"mjo\", \"cmip6\", \"historical\")\n", + "json_url_list" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/qbo-mjo/cmip6/historical/v20240422/QBO_MJO_cmip6_historical_v20240422.json']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_url_list = find_pmp_archive_json_urls(\"qbo-mjo\", \"cmip6\", \"historical\")\n", + "json_url_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrive Model Metrics from Data Base Access" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 JSON files for metric 'enso_metric' and collected info for model 'ACCESS-CM2'.\n", + "Found 29 JSON files for metric 'mean_climate' and collected info for model 'ACCESS-CM2'.\n", + "Found 1 JSON files for metric 'mjo' and collected info for model 'ACCESS-CM2'.\n", + "Found 7 JSON files for metric 'variability_modes' and collected info for model 'ACCESS-CM2'.\n", + "Found 1 JSON files for metric 'qbo-mjo' and collected info for model 'ACCESS-CM2'.\n" + ] + } + ], + "source": [ + "results_dict = database_metrics(\"cmip6\", \"ACCESS-CM2\", \"historical\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics = list(results_dict.keys())\n", + "metrics " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['ENSO_perf', 'ENSO_proc', 'ENSO_tel'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_dict[\"enso_metric\"].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['rlutcs', 'ts', 'tas', 'psl', 'rsut', 'va', 'tauu', 'ta', 'rsutcs', 'rstcre', 'rt', 'rldscs', 'rltcre', 'rsds', 'prw', 'rlut', 'rsus', 'ua', 'zg', 'rlds', 'tauv', 'rsdt', 'sfcWind', 'pr', 'rlus', 'rsdscs'])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_dict[\"mean_climate\"].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "results_dict\n", + "with open('results_dict.json', 'w') as json_file:\n", + " json.dump(results_dict, json_file, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'enso_metric': {'ENSO_perf': {'REFERENCE': 'MC for ENSO Performance...',\n", + " 'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Meridional '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'nino3_LatExt '\n", + " 'pr, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'Meridional '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'pr, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'sst, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'the '\n", + " 'duration '\n", + " 'is '\n", + " 'the '\n", + " 'number '\n", + " 'of '\n", + " 'consecutive '\n", + " 'months '\n", + " 'during '\n", + " 'which '\n", + " 'the '\n", + " 'regression '\n", + " 'is '\n", + " 'above '\n", + " '0.25, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Duration '\n", + " 'based '\n", + " 'on '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'zonal '\n", + " 'SSTA '\n", + " '(meridional '\n", + " 'averaged '\n", + " '[-5.0 '\n", + " '; '\n", + " '5.0]), '\n", + " 'the '\n", + " 'zonal '\n", + " 'SSTA '\n", + " 'maximum '\n", + " '(minimum) '\n", + " 'is '\n", + " 'located '\n", + " 'for '\n", + " 'each '\n", + " 'event, '\n", + " 'the '\n", + " 'diversity '\n", + " 'is '\n", + " 'the '\n", + " 'interquartile '\n", + " 'range '\n", + " '(IQR '\n", + " '= '\n", + " 'Q3 '\n", + " '- '\n", + " 'Q1), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Diversity '\n", + " '(interquartile '\n", + " 'range)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Meridional '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'nino3_LatExt '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'meridional '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'sst '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'taux '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'CMAP': {'value': 2.0869991859619423,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 1.655447234881955,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0647494381009803,\n", + " 'value_error': None},\n", + " 'TRMM-3B43v-7': {'value': 2.024030502343981,\n", + " 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'CMAP': {'value': 0.6446395021127977,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 1.0210307836213295,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4915585903492389,\n", + " 'value_error': None},\n", + " 'TRMM-3B43v-7': {'value': 0.5911642243012007,\n", + " 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.5086063943211802,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.49792070738173966,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.4665249564478334,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.5918436453916678,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5144345310648003,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.6144647650906735,\n", + " 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.905344203497415,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.641704430321212,\n", + " 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,\n", + " 'value_error': 0.06289844115817948},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 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'\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'the '\n", + " 'duration '\n", + " 'is '\n", + " 'the '\n", + " 'number '\n", + " 'of '\n", + " 'consecutive '\n", + " 'months '\n", + " 'during '\n", + " 'which '\n", + " 'the '\n", + " 'regression '\n", + " 'is '\n", + " 'above '\n", + " '0.25, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Duration '\n", + " 'based '\n", + " 'on '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'zonal '\n", + " 'SSTA '\n", + " '(meridional '\n", + " 'averaged '\n", + " '[-5.0 '\n", + " '; '\n", + " '5.0]), '\n", + " 'the '\n", + " 'zonal '\n", + " 'SSTA '\n", + " 'maximum '\n", + " '(minimum) '\n", + " 'is '\n", + " 'located '\n", + " 'for '\n", + " 'each '\n", + " 'event, '\n", + " 'the '\n", + " 'diversity '\n", + " 'is '\n", + " 'the '\n", + " 'interquartile '\n", + " 'range '\n", + " '(IQR '\n", + " '= '\n", + " 'Q3 '\n", + " '- '\n", + " 'Q1), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Diversity '\n", + " '(interquartile '\n", + " 'range)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Meridional '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'nino3_LatExt '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'meridional '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'sst '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'taux '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations 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'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'pr, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'sst, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'the '\n", + " 'duration '\n", + " 'is '\n", + " 'the '\n", + " 'number '\n", + " 'of '\n", + " 'consecutive '\n", + " 'months '\n", + " 'during '\n", + " 'which '\n", + " 'the '\n", + " 'regression '\n", + " 'is '\n", + " 'above '\n", + " '0.25, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Duration '\n", + " 'based '\n", + " 'on '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'zonal '\n", + " 'SSTA '\n", + " '(meridional '\n", + " 'averaged '\n", + " '[-5.0 '\n", + " '; '\n", + " '5.0]), '\n", + " 'the '\n", + " 'zonal '\n", + " 'SSTA '\n", + " 'maximum '\n", + " '(minimum) '\n", + " 'is '\n", + " 'located '\n", + " 'for '\n", + " 'each '\n", + " 'event, '\n", + " 'the '\n", + " 'diversity '\n", + " 'is '\n", + " 'the '\n", + " 'interquartile '\n", + " 'range '\n", + " '(IQR '\n", + " '= '\n", + " 'Q3 '\n", + " '- '\n", + " 'Q1), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'Diversity '\n", + " '(interquartile '\n", + " 'range)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " '6 '\n", + " 'years '\n", + " '(centered '\n", + " 'on '\n", + " 'ENSO), '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'ENSO '\n", + " 'life '\n", + " 'cyle '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Meridional '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'nino3_LatExt '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'meridional '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'CMAP': {'name': 'CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'climatological '\n", + " 'pr '\n", + " 'STD, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'pr '\n", + " 'zonal '\n", + " 'seasonality '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 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{'20CRv2': {'value': 0.2554585558369024,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.2670068037258789,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.2631029492278568,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.2506751949918031,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2591660014479898,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.2593749232159955,\n", + " 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.587471160907709,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.2934249066850017,\n", + " 'value_error': None}}}}}}},\n", + " 'provenance': {'commandLine': '/home/lee1043/.conda/envs/pmp_nightly_20210620/bin/enso_driver.py '\n", + " '-p '\n", + " '../param/my_Param_ENSO_PCMDIobs.py '\n", + " '--mip cmip6 '\n", + " '--metricsCollection '\n", + " 'ENSO_perf '\n", + " '--case_id '\n", + " 'v20210620 '\n", + " '--modnames '\n", + " 'UKESM1-0-LL '\n", + " '--realization '\n", + " 'r9i1p1f2',\n", + " 'conda': {'Platform': 'linux-64',\n", + " 'PythonVersion': '3.7.3.final.0',\n", + " 'Version': '4.8.3',\n", + " 'buildVersion': '3.18.8'},\n", + " 'date': '2021-06-22 07:31:40',\n", + " 'history': 'import EnsoMetrics\\n'\n", + " 'from '\n", + " '...script.PMPdriver_lib '\n", + " 'import '\n", + " 'AddParserArgument\\n'\n", + " 'from '\n", + " '...script.PMPdriver_lib '\n", + " 'import '\n", + " 'AddParserArgument\\n'\n", + " 'from '\n", + " 'script.PMPdriver_lib '\n", + " 'import '\n", + " 'AddParserArgument\\n'\n", + " 'from '\n", + " 'script.PMPdriver_libfrom '\n", + " 'PMPdriver_lib import '\n", + " 'AddParserArgument\\n'\n", + " ' import '\n", + " 'AddParserArgument\\n'\n", + " 'from PMPdriver_lib '\n", + " 'import '\n", + " 'AddParserArgument\\n',\n", + " 'openGL': {'GLX': {'client': {},\n", + " 'server': {}}},\n", + " 'osAccess': False,\n", + " 'packages': {'PMP': 'v2.0-15-g182be71',\n", + " 'PMPObs': 'See '\n", + " \"'References' \"\n", + " 'key '\n", + " 'below, '\n", + " 'for '\n", + " 'detailed '\n", + " 'obs '\n", + " 'provenance '\n", + " 'information.',\n", + " 'blas': '0.3.10',\n", + " 'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',\n", + " 'cdms': '3.1.5.2020.11.03.21.54.gf997653',\n", + " 'cdp': '1.7.0',\n", + " 'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',\n", + " 'cdutil': '8.2.2020.09.28.17.09.g484910c',\n", + " 'clapack': None,\n", + " 'esmf': '8.0.1',\n", + " 'esmpy': '8.0.1',\n", + " 'genutil': '8.2.2020.10.07.17.46.ge34ccd5',\n", + " 'lapack': '3.8.0',\n", + " 'matplotlib': '3.4.2',\n", + " 'mesalib': None,\n", + " 'numpy': '1.20.3',\n", + " 'python': '3.8.10',\n", + " 'scipy': '1.5.2',\n", + " 'uvcdat': None,\n", + " 'vcs': '8.2.2020.08.06.20.48.g4abe712',\n", + " 'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},\n", + " 'platform': {'Name': 'gates.llnl.gov',\n", + " 'OS': 'Linux',\n", + " 'Version': '3.10.0-1160.31.1.el7.x86_64'},\n", + " 'userId': 'lee1043'}},\n", + " 'ENSO_proc': {'REFERENCE': 'MC for ENSO Process...',\n", + " 'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'sst, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,\n", + " 'name': '20CRv2_AVISO',\n", + " 'nyears': 20,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-20C_AVISO',\n", + " 'nyears': 18,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-5_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_AVISO': {'keyerror': None,\n", + " 'name': 'HadISST_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sstA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sshA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA>0',\n", + " 'name': 'Sst-Ssh '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/cm'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSshSst',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_Tropflux': {'keyerror': None,\n", + " 'name': '20CRv2_Tropflux',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-20C_Tropflux',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-5_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-Interim_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_Tropflux': {'keyerror': None,\n", + " 'name': 'HadISST_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2017-7-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino4 '\n", + " 'tauxA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Taux-Sst '\n", + " 'feedback '\n", + " '(mu)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstTaux',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': '',\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': '',\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': '',\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'thfA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Thf-Sst '\n", + " 'feedback '\n", + " '(alpha)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'W/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstThf',\n", + " 'units': '%'}},\n", + " 'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sshA '\n", + " 'over '\n", + " 'nino4 '\n", + " 'tauxA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA>0',\n", + " 'name': 'Ssh-Taux '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e3 '\n", + " 'cm/N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbTauxSsh',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'dSSToce '\n", + " '= '\n", + " 'dSST '\n", + " '- '\n", + " 'dSSTthf '\n", + " 'during '\n", + " 'ENSO '\n", + " 'events '\n", + " '(relative '\n", + " 'difference '\n", + " 'between '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'and '\n", + " 'heat '\n", + " 'flux-driven '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'in '\n", + " ', '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'SST '\n", + " 'change '\n", + " 'caused '\n", + " 'by '\n", + " 'an '\n", + " 'anomalous '\n", + " 'ocean '\n", + " 'circulation '\n", + " '(dSSToce)',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsodSstOce',\n", + " 'units': '%'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'processes'},\n", + " 'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.5086063943211802,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.49792070738173966,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.4665249564478334,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.5918436453916678,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5144345310648003,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.6144647650906735,\n", + " 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.905344203497415,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.641704430321212,\n", + " 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,\n", + " 'value_error': 0.06289844115817948},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 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0.21158996233933872},\n", + " 'ERA-20C': {'value': 1.5951589911955568,\n", + " 'value_error': 0.30349823329934555},\n", + " 'ERA-5': {'value': 2.0283123524204223,\n", + " 'value_error': 0.6454942543282691},\n", + " 'ERA-Interim': {'value': 2.052960044692134,\n", + " 'value_error': 0.6533381861195713},\n", + " 'HadISST': {'value': 1.6666267700450468,\n", + " 'value_error': 0.273531261566947},\n", + " 'Tropflux': {'value': 2.06093854374807,\n", + " 'value_error': 0.6643426635460994}},\n", + " 'metric': {'20CRv2': {'value': 17.445859507674687,\n", + " 'value_error': 26.75332883692444},\n", + " 'ERA-20C': {'value': 14.936639481415629,\n", + " 'value_error': 29.448836215769177},\n", + " 'ERA-5': {'value': 33.10222452148056,\n", + " 'value_error': 31.72150771348471},\n", + " 'ERA-Interim': {'value': 33.90539445550813,\n", + " 'value_error': 31.340661548193676},\n", + " 'HadISST': {'value': 18.584300461667556,\n", + " 'value_error': 26.057864916471747},\n", + " 'Tropflux': {'value': 34.16126610657646,\n", + " 'value_error': 31.489767742015175}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.0659544064205628,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.0848741234618347,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.055882763305978356,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.06132020021246395,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07329593603923167,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.06022298994696265,\n", + " 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,\n", + " 'value_error': 0.029761039242344564},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': -0.28449736152546684,\n", + " 'value_error': -0.0221480895564315},\n", + " 'ERA-20C': {'value': 0.19142977450459012,\n", + " 'value_error': 0.01816971010961296},\n", + " 'ERA-5': {'value': 0.473403564017451,\n", + " 'value_error': 0.07485167573682382},\n", + " 'ERA-Interim': {'value': 0.40501535626049495,\n", + " 'value_error': 0.06403855065638503},\n", + " 'HadISST': {'value': 0.40320728014992363,\n", + " 'value_error': 0.033032027448448076},\n", + " 'Tropflux': {'value': 0.3838870736969205,\n", + " 'value_error': 0.061471128380725305}},\n", + " 'metric': {'20CRv2': {'value': 180.2206133773295,\n", + " 'value_error': -12.977130611162165},\n", + " 'ERA-20C': {'value': 248.6170906598676,\n", + " 'value_error': -25.675934806835166},\n", + " 'ERA-5': {'value': 160.09615962987965,\n", + " 'value_error': -14.180516834976428},\n", + " 'ERA-Interim': {'value': 170.2435987001159,\n", + " 'value_error': -16.574944889175054},\n", + " 'HadISST': {'value': 170.55858748871867,\n", + " 'value_error': -11.273363299898966},\n", + " 'Tropflux': {'value': 174.10964864893523,\n", + " 'value_error': -17.636469539835424}}},\n", + " 'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'value': 2.363055944556165,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': 1.621685462712441,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': 2.44408374051583,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': 2.4614131442372607,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': 1.8011858193585126,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': 2.3814159901043817,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': 1.6738291099743765,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2_ERA-Interim': {'value': 31.373378338825997,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': 33.64853111087837,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': 34.11567389614446,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': 9.965676762323174,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': 31.90247023404929,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': 3.115231235447546,\n", + " 'value_error': None}}}}},\n", + " 'r2i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'sst, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,\n", + " 'name': '20CRv2_AVISO',\n", + " 'nyears': 20,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-20C_AVISO',\n", + " 'nyears': 18,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-5_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_AVISO': {'keyerror': None,\n", + " 'name': 'HadISST_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sstA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sshA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA>0',\n", + " 'name': 'Sst-Ssh '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/cm'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSshSst',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_Tropflux': {'keyerror': None,\n", + " 'name': '20CRv2_Tropflux',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-20C_Tropflux',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-5_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-Interim_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_Tropflux': {'keyerror': None,\n", + " 'name': 'HadISST_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2017-7-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino4 '\n", + " 'tauxA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Taux-Sst '\n", + " 'feedback '\n", + " '(mu)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstTaux',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': '',\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': '',\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': '',\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'thfA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Thf-Sst '\n", + " 'feedback '\n", + " '(alpha)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'W/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstThf',\n", + " 'units': '%'}},\n", + " 'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sshA '\n", + " 'over '\n", + " 'nino4 '\n", + " 'tauxA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA>0',\n", + " 'name': 'Ssh-Taux '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e3 '\n", + " 'cm/N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbTauxSsh',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'dSSToce '\n", + " '= '\n", + " 'dSST '\n", + " '- '\n", + " 'dSSTthf '\n", + " 'during '\n", + " 'ENSO '\n", + " 'events '\n", + " '(relative '\n", + " 'difference '\n", + " 'between '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'and '\n", + " 'heat '\n", + " 'flux-driven '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'in '\n", + " ', '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'SST '\n", + " 'change '\n", + " 'caused '\n", + " 'by '\n", + " 'an '\n", + " 'anomalous '\n", + " 'ocean '\n", + " 'circulation '\n", + " '(dSSToce)',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsodSstOce',\n", + " 'units': '%'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'processes'},\n", + " 'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.5390012963087842,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.526127105192347,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.4945591581204164,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.6258387652235506,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5446363993051538,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.6491058945682061,\n", + " 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.301982440374169,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.046539147746591,\n", + " 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': 0.8622105536310941,\n", + " 'value_error': 0.0671230005646729},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 0.1423236985494241},\n", + " 'HadISST': {'value': 0.7688706055408969,\n", + " 'value_error': 0.06298833428079066},\n", + " 'Tropflux': {'value': 0.9128364190673677,\n", + " 'value_error': 0.14617081051130443}},\n", + " 'metric': {'20CRv2': {'value': 16.389741161998415,\n", + " 'value_error': 18.828139168597463},\n", + " 'ERA-20C': {'value': 4.319101721274182,\n", + " 'value_error': 18.022762005435613},\n", + " 'ERA-5': {'value': 5.000098560096893,\n", + " 'value_error': 22.4165355987215},\n", + " 'ERA-Interim': {'value': 4.213100140796893,\n", + " 'value_error': 22.602238718566696},\n", + " 'HadISST': {'value': 12.139877297628379,\n", + " 'value_error': 17.916934311991206},\n", + " 'Tropflux': {'value': 5.5459953589491215,\n", + " 'value_error': 22.47797967115718}}},\n", + " 'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'nonlinearity': -0.04152801980348014,\n", + " 'nonlinearity_error': 0.017513672368536456,\n", + " 'value': 0.11810260256238658,\n", + " 'value_error': 0.003759915555702643},\n", + " 'ACCESS-CM2_r2i1p1f1': {'nonlinearity': 0.03972377669451063,\n", + " 'nonlinearity_error': 0.015497896313684197,\n", + " 'value': 0.17736383623953692,\n", + " 'value_error': 0.003272883265824272},\n", + " 'ERA-20C_AVISO': {'nonlinearity': -0.032011742613362676,\n", + " 'nonlinearity_error': 0.0183450811389117,\n", + " 'value': 0.12657346976318096,\n", + " 'value_error': 0.003913658970403057},\n", + " 'ERA-5_AVISO': {'nonlinearity': -0.02566396021245236,\n", + " 'nonlinearity_error': 0.015885984596935174,\n", + " 'value': 0.12628612766653574,\n", + " 'value_error': 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'value_error': 6.427235694573791},\n", + " 'HadISST_AVISO': {'value': 44.920614650721646,\n", + " 'value_error': 6.637031660497709},\n", + " 'Tropflux_AVISO': {'value': 38.738629471118394,\n", + " 'value_error': 6.199018841348387}}},\n", + " 'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 4.934033160844237,\n", + " 'nonlinearity_error': 2.902746466279199,\n", + " 'value': 14.279399855092704,\n", + " 'value_error': 0.6474408536852889},\n", + " '20CRv2_Tropflux': {'nonlinearity': 4.246457468336489,\n", + " 'nonlinearity_error': 3.158751303915279,\n", + " 'value': 14.953358496715085,\n", + " 'value_error': 0.7041991995478306},\n", + " 'ACCESS-CM2_r2i1p1f1': {'nonlinearity': -3.2458665385371583,\n", + " 'nonlinearity_error': 1.0129505091547086,\n", + " 'value': 6.443086075760712,\n", + " 'value_error': 0.20610779643757524},\n", + " 'ERA-20C_ERA-Interim': {'nonlinearity': 4.935065161864337,\n", + " 'nonlinearity_error': 2.7819513240604192,\n", + " 'value': 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29.957326685328788},\n", + " 'ERA-Interim': {'value': 37.58122380820546,\n", + " 'value_error': 29.597661151977473},\n", + " 'HadISST': {'value': 23.11220732589619,\n", + " 'value_error': 24.608665485754038},\n", + " 'Tropflux': {'value': 37.822865243089396,\n", + " 'value_error': 29.738474854764302}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.07871049480199414,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.09219488954801747,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.06736418555616827,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.0733895852726686,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08581263382615822,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.07200721549200347,\n", + " 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,\n", + " 'value_error': 0.029761039242344564},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': -0.1849490598954878,\n", + " 'value_error': -0.014398264785230352},\n", + " 'ERA-20C': {'value': 0.19142977450459012,\n", + " 'value_error': 0.01816971010961296},\n", + " 'ERA-5': {'value': 0.473403564017451,\n", + " 'value_error': 0.07485167573682382},\n", + " 'ERA-Interim': {'value': 0.40501535626049495,\n", + " 'value_error': 0.06403855065638503},\n", + " 'HadISST': {'value': 0.40320728014992363,\n", + " 'value_error': 0.033032027448448076},\n", + " 'Tropflux': {'value': 0.3838870736969205,\n", + " 'value_error': 0.061471128380725305}},\n", + " 'metric': {'20CRv2': {'value': 152.15066652577153,\n", + " 'value_error': -8.436310599880745},\n", + " 'ERA-20C': {'value': 196.61457334634903,\n", + " 'value_error': -16.69168381386522},\n", + " 'ERA-5': {'value': 139.06794835382146,\n", + " 'value_error': -9.21862066979577},\n", + " 'ERA-Interim': {'value': 145.66470308758701,\n", + " 'value_error': -10.775215835518514},\n", + " 'HadISST': {'value': 145.86947433754634,\n", + " 'value_error': -7.328707489577084},\n", + " 'Tropflux': {'value': 148.17798581087555,\n", + " 'value_error': -11.465303030502733}}},\n", + " 'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'value': 2.363055944556165,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': 1.9242936137047122,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': 2.44408374051583,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': 2.4614131442372607,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': 1.8011858193585126,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': 2.3814159901043817,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': 1.6738291099743765,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2_ERA-Interim': {'value': 18.56758118072665,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': 21.267279766012223,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': 21.821591868478883,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': 6.834819207606468,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': 19.195402159856705,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': 14.96356481302741,\n", + " 'value_error': None}}}}},\n", + " 'r3i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'sst, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'sst '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Zonal '\n", + " 'root '\n", + " 'mean '\n", + " 'square '\n", + " 'error '\n", + " 'of '\n", + " 'equatorial_pacific '\n", + " 'taux, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'taux '\n", + " 'Zonal '\n", + " 'RMSE',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 '\n", + " 'N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,\n", + " 'name': '20CRv2_AVISO',\n", + " 'nyears': 20,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-20C_AVISO',\n", + " 'nyears': 18,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-5_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_AVISO': {'keyerror': None,\n", + " 'name': 'HadISST_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sstA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sshA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sshA>0',\n", + " 'name': 'Sst-Ssh '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/cm'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSshSst',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_Tropflux': {'keyerror': None,\n", + " 'name': '20CRv2_Tropflux',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-20C_Tropflux',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-5_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_Tropflux': {'keyerror': None,\n", + " 'name': 'ERA-Interim_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_Tropflux': {'keyerror': None,\n", + " 'name': 'HadISST_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2017-7-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux_Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino4 '\n", + " 'tauxA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Taux-Sst '\n", + " 'feedback '\n", + " '(mu)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 '\n", + " 'N/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstTaux',\n", + " 'units': '%'}},\n", + " 'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': '',\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': '',\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': '',\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': '',\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'thfA '\n", + " 'over '\n", + " 'nino3 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'sstA>0',\n", + " 'name': 'Thf-Sst '\n", + " 'feedback '\n", + " '(alpha)',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'W/m2/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbSstThf',\n", + " 'units': '%'}},\n", + " 'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_AVISO': {'keyerror': None,\n", + " 'name': 'ERA-Interim_AVISO',\n", + " 'nyears': 26,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_AVISO': {'keyerror': None,\n", + " 'name': 'Tropflux_AVISO',\n", + " 'nyears': 25,\n", + " 'time_period': ['1993-1-16 '\n", + " '12:0:0.0',\n", + " '2017-7-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'Regression '\n", + " 'of '\n", + " 'nino3 '\n", + " 'sshA '\n", + " 'over '\n", + " 'nino4 '\n", + " 'tauxA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'method_nonlinearity': 'The '\n", + " 'nonlinearity '\n", + " 'is '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA<0 '\n", + " 'minus '\n", + " 'the '\n", + " 'regression '\n", + " 'computed '\n", + " 'when '\n", + " 'tauxA>0',\n", + " 'name': 'Ssh-Taux '\n", + " 'feedback',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e3 '\n", + " 'cm/N/m2'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"ERA-Interim's \"\n", + " 'tauu; '\n", + " \"Tropflux's \"\n", + " 'tauu; '\n", + " \"AVISO's \"\n", + " 'zos',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoFbTauxSsh',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'skewness',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,\n", + " 'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'keyerror': None,\n", + " 'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'keyerror': None,\n", + " 'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Nino '\n", + " '(Nina) '\n", + " 'events '\n", + " '= '\n", + " 'nino3.4 '\n", + " 'sstA '\n", + " '> '\n", + " '0.75 '\n", + " '(< '\n", + " '-0.75) '\n", + " 'during '\n", + " 'DEC, '\n", + " 'dSSToce '\n", + " '= '\n", + " 'dSST '\n", + " '- '\n", + " 'dSSTthf '\n", + " 'during '\n", + " 'ENSO '\n", + " 'events '\n", + " '(relative '\n", + " 'difference '\n", + " 'between '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'and '\n", + " 'heat '\n", + " 'flux-driven '\n", + " 'nino3 '\n", + " 'SST '\n", + " 'change '\n", + " 'in '\n", + " ', '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points',\n", + " 'name': 'SST '\n", + " 'change '\n", + " 'caused '\n", + " 'by '\n", + " 'an '\n", + " 'anomalous '\n", + " 'ocean '\n", + " 'circulation '\n", + " '(dSSToce)',\n", + " 'ref': 'Using '\n", + " 'CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'hfls '\n", + " '& '\n", + " 'hfss '\n", + " '& '\n", + " 'rlds '\n", + " '& '\n", + " 'rlus '\n", + " '& '\n", + " 'rsds '\n", + " '& '\n", + " 'rsus',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsodSstOce',\n", + " 'units': '%'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'processes'},\n", + " 'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r3i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.541184671035061,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 0.5312798674415361,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.5002622936625699,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.6127163520938992,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5481567062502946,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 0.6312817570271734,\n", + " 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.318457710429736,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.017214896301271,\n", + " 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r3i1p1f1': {'value': 0.9173210955753004,\n", + " 'value_error': 0.07141335043624629},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 0.1423236985494241},\n", + " 'HadISST': {'value': 0.7688706055408969,\n", + " 'value_error': 0.06298833428079066},\n", + " 'Tropflux': {'value': 0.9128364190673677,\n", + " 'value_error': 0.14617081051130443}},\n", + " 'metric': {'20CRv2': {'value': 23.829109289853704,\n", + " 'value_error': 20.031591096914156},\n", + " 'ERA-20C': {'value': 10.986941968393225,\n", + " 'value_error': 19.174736053152937},\n", + " 'ERA-5': {'value': 1.072079553416787,\n", + " 'value_error': 23.849349683580996},\n", + " 'ERA-Interim': {'value': 1.9093811257160185,\n", + " 'value_error': 24.046922525424254},\n", + " 'HadISST': {'value': 19.307603771634533,\n", + " 'value_error': 19.062144093701967},\n", + " 'Tropflux': {'value': 0.49129027000419734,\n", + " 'value_error': 23.914721121689627}}},\n", + " 'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'nonlinearity': -0.04152801980348014,\n", + " 'nonlinearity_error': 0.017513672368536456,\n", + " 'value': 0.11810260256238658,\n", + " 'value_error': 0.003759915555702643},\n", + " 'ACCESS-CM2_r3i1p1f1': {'nonlinearity': 0.041627092093874246,\n", + " 'nonlinearity_error': 0.014558494264799928,\n", + " 'value': 0.1663685576882898,\n", + " 'value_error': 0.0032137028841764557},\n", + " 'ERA-20C_AVISO': {'nonlinearity': -0.032011742613362676,\n", + " 'nonlinearity_error': 0.0183450811389117,\n", + " 'value': 0.12657346976318096,\n", + " 'value_error': 0.003913658970403057},\n", + " 'ERA-5_AVISO': {'nonlinearity': -0.02566396021245236,\n", + " 'nonlinearity_error': 0.015885984596935174,\n", + " 'value': 0.12628612766653574,\n", + " 'value_error': 0.0034955457677469676},\n", + " 'ERA-Interim_AVISO': {'nonlinearity': -0.01795688795240266,\n", + " 'nonlinearity_error': 0.015596453142222452,\n", + " 'value': 0.12602582769944173,\n", + " 'value_error': 0.0034298901844770405},\n", + " 'HadISST_AVISO': {'nonlinearity': -0.027397296086239156,\n", + " 'nonlinearity_error': 0.01524011202550052,\n", + " 'value': 0.12238689206984654,\n", + " 'value_error': 0.0033466415532667315},\n", + " 'Tropflux_AVISO': {'nonlinearity': -0.015122526459173655,\n", + " 'nonlinearity_error': 0.01524840165408661,\n", + " 'value': 0.1278402683633683,\n", + " 'value_error': 0.0033530380648741243}},\n", + " 'metric': {'20CRv2_AVISO': {'value': 40.8678166938846,\n", + " 'value_error': 7.2057801033919375},\n", + " 'ERA-20C_AVISO': {'value': 31.44030735632472,\n", + " 'value_error': 6.603143833688139},\n", + " 'ERA-5_AVISO': {'value': 31.739376891493198,\n", + " 'value_error': 6.191268385557707},\n", + " 'ERA-Interim_AVISO': {'value': 32.0114779051967,\n", + " 'value_error': 6.142829409295891},\n", + " 'HadISST_AVISO': {'value': 35.93658183046497,\n", + " 'value_error': 6.343010175775079},\n", + " 'Tropflux_AVISO': {'value': 30.13783514237483,\n", + " 'value_error': 5.9271418390378106}}},\n", + " 'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 4.934033160844237,\n", + " 'nonlinearity_error': 2.902746466279199,\n", + " 'value': 14.279399855092704,\n", + " 'value_error': 0.6474408536852889},\n", + " '20CRv2_Tropflux': {'nonlinearity': 4.246457468336489,\n", + " 'nonlinearity_error': 3.158751303915279,\n", + " 'value': 14.953358496715085,\n", + " 'value_error': 0.7041991995478306},\n", + " 'ACCESS-CM2_r3i1p1f1': {'nonlinearity': -2.9954687827320576,\n", + " 'nonlinearity_error': 0.9985754067182548,\n", + " 'value': 6.288464480125982,\n", + " 'value_error': 0.21429541399281996},\n", + " 'ERA-20C_ERA-Interim': {'nonlinearity': 4.935065161864337,\n", + " 'nonlinearity_error': 2.7819513240604192,\n", + " 'value': 13.745001697775006,\n", + " 'value_error': 0.6142727838878883},\n", + " 'ERA-20C_Tropflux': {'nonlinearity': 4.42190934284047,\n", + " 'nonlinearity_error': 2.9683595503356464,\n", + " 'value': 14.35072947741424,\n", + " 'value_error': 0.6543595156569981},\n", + " 'ERA-5_ERA-Interim': {'nonlinearity': 3.1738377820903914,\n", + " 'nonlinearity_error': 2.4631712135067945,\n", + " 'value': 13.283692727594216,\n", + " 'value_error': 0.5482165088552355},\n", + " 'ERA-5_Tropflux': {'nonlinearity': 2.619020521914347,\n", + " 'nonlinearity_error': 2.7583727584713595,\n", + " 'value': 14.195776796140159,\n", + " 'value_error': 0.6128603267022297},\n", + " 'ERA-Interim_ERA-Interim': {'nonlinearity': 2.8547933106941965,\n", + " 'nonlinearity_error': 2.4777477618225165,\n", + " 'value': 13.423273078794779,\n", + " 'value_error': 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'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': ''}},\n", + " 'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C'}},\n", + " 'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C '\n", + " '/ '\n", + " 'mm/day/C'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': ''}},\n", + " 'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C '\n", + " '/ '\n", + " 'C/C'}},\n", + " 'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': ''}},\n", + " 'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r1i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r1i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': 'C/C '\n", + " '/ '\n", + " 'C/C'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'teleconnections'},\n", + " 'value': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,\n", + " 'value_error': 0.06289844115817948},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 0.1423236985494241},\n", + " 'HadISST': {'value': 0.7688706055408969,\n", + " 'value_error': 0.06298833428079066},\n", + " 'Tropflux': {'value': 0.9128364190673677,\n", + " 'value_error': 0.14617081051130443}},\n", + " 'metric': {'20CRv2': {'value': 9.064452189383056,\n", + " 'value_error': 17.643141600516042},\n", + " 'ERA-20C': {'value': 2.2464903819711926,\n", + " 'value_error': 16.888452929253955},\n", + " 'ERA-5': {'value': 10.97916272391069,\n", + " 'value_error': 21.005693033164114},\n", + " 'ERA-Interim': {'value': 10.241696082796707,\n", + " 'value_error': 21.17970844752598},\n", + " 'HadISST': {'value': 5.082064483907947,\n", + " 'value_error': 16.789285775029295},\n", + " 'Tropflux': {'value': 11.490702097779455,\n", + " 'value_error': 21.06326996420269}}},\n", + " 'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2_CMAP': {'value': 0.563236858039843,\n", + " 'value_error': None},\n", + " '20CRv2_ERA-Interim': {'value': 0.5076711289090614,\n", + " 'value_error': None},\n", + " 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None}}},\n", + " 'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r1i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.8940403791539789,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 1.3357218503755024,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.8063148379784217,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.8370831952681334,\n", + " 'value_error': None}}}}},\n", + " 'r2i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': ''}},\n", + " 'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C'}},\n", + " 'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C '\n", + " '/ '\n", + " 'mm/day/C'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': ''}},\n", + " 'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C '\n", + " '/ '\n", + " 'C/C'}},\n", + " 'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': ''}},\n", + " 'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r2i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r2i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapJja',\n", + " 'units': 'C/C '\n", + " '/ '\n", + " 'C/C'}}},\n", + " 'name': 'Metrics '\n", + " 'Collection '\n", + " 'for '\n", + " 'ENSO '\n", + " 'teleconnections'},\n", + " 'value': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,\n", + " 'value_error': 0.062166219832622445},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': 0.8622105536310941,\n", + " 'value_error': 0.0671230005646729},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 0.1423236985494241},\n", + " 'HadISST': {'value': 0.7688706055408969,\n", + " 'value_error': 0.06298833428079066},\n", + " 'Tropflux': {'value': 0.9128364190673677,\n", + " 'value_error': 0.14617081051130443}},\n", + " 'metric': {'20CRv2': {'value': 16.389741161998415,\n", + " 'value_error': 18.828139168597463},\n", + " 'ERA-20C': {'value': 4.319101721274182,\n", + " 'value_error': 18.022762005435613},\n", + " 'ERA-5': {'value': 5.000098560096893,\n", + " 'value_error': 22.4165355987215},\n", + " 'ERA-Interim': {'value': 4.213100140796893,\n", + " 'value_error': 22.602238718566696},\n", + " 'HadISST': {'value': 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'ERA-20C': {'value': 0.20728092389695468,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.27297038856869105,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.26189095419809955,\n", + " 'value_error': None}}},\n", + " 'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r2i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None,\n", + " 'value_error': None}},\n", + " 'metric': {'20CRv2': {'value': 0.8655671977221406,\n", + " 'value_error': None},\n", + " 'ERA-20C': {'value': 1.2931821044367315,\n", + " 'value_error': None},\n", + " 'ERA-5': {'value': 0.7806355183321791,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 0.8104239724319492,\n", + " 'value_error': None}}}}},\n", + " 'r3i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '\n", + " 'which '\n", + " 'science '\n", + " 'question '\n", + " 'this '\n", + " 'collection '\n", + " 'is '\n", + " 'about',\n", + " 'metrics': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Standard '\n", + " 'deviation '\n", + " 'of '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'amplitude',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regression '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoPrMapDjfCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapDjf',\n", + " 'units': ''}},\n", + " 'EnsoPrMapDjfRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapDjf',\n", + " 'units': 'mm/day/C'}},\n", + " 'EnsoPrMapDjfStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapDjf',\n", + " 'units': 'mm/day/C '\n", + " '/ '\n", + " 'mm/day/C'}},\n", + " 'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': ''}},\n", + " 'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C'}},\n", + " 'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',\n", + " 'nyears': 34,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " '20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',\n", + " 'nyears': 15,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',\n", + " 'nyears': 32,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',\n", + " 'nyears': 13,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_CMAP': {'name': 'ERA-5_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '12:0:0.0',\n", + " '2017-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST_CMAP': {'name': 'HadISST_CMAP',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '3:0:0.0',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-16 '\n", + " '3:0:0.0',\n", + " '2017-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux_CMAP': {'name': 'Tropflux_CMAP',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',\n", + " 'nyears': 20,\n", + " 'time_period': ['1998-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'precipitation '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'PRA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"CMAP's \"\n", + " 'pr; '\n", + " \"ERA-Interim's \"\n", + " 'pr; '\n", + " \"GPCPv2.3's \"\n", + " 'pr; '\n", + " \"TRMM-3B43v-7's \"\n", + " 'pr',\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoPrMapJja',\n", + " 'units': 'mm/day/C '\n", + " '/ '\n", + " 'mm/day/C'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,\n", + " 'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'keyerror': None,\n", + " 'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'keyerror': None,\n", + " 'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'keyerror': 'unlikely '\n", + " 'units: '\n", + " 'K([-1e+30, '\n", + " '304.7203])',\n", + " 'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'Ratio '\n", + " 'between '\n", + " 'NDJ '\n", + " 'and '\n", + " 'MAM '\n", + " 'standard '\n", + " 'deviation '\n", + " 'nino3.4 '\n", + " 'sstA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended',\n", + " 'name': 'ENSO '\n", + " 'seasonality',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'std '\n", + " 'dev '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'absolute '\n", + " 'value '\n", + " 'of '\n", + " 'the '\n", + " 'relative '\n", + " 'difference '\n", + " 'between '\n", + " 'model '\n", + " 'and '\n", + " 'observations '\n", + " 'values '\n", + " '(M '\n", + " '= '\n", + " '100 '\n", + " '* '\n", + " 'abs[[model-obs] '\n", + " '/ '\n", + " 'obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 149,\n", + " 'time_period': ['1870-1-16 '\n", + " '11:59:59.5',\n", + " '2018-12-16 '\n", + " '18:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 39,\n", + " 'time_period': ['1979-1-15 '\n", + " '0:0:0.0',\n", + " '2017-7-15 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'during '\n", + " 'DEC '\n", + " 'regressed '\n", + " 'against '\n", + " 'equatorial_pacific '\n", + " 'SSTA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'smoothing '\n", + " 'using '\n", + " 'a '\n", + " 'triangle '\n", + " 'shaped '\n", + " 'window '\n", + " 'of '\n", + " '5 '\n", + " 'points, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'Zonal '\n", + " 'SSTA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': ''}},\n", + " 'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'DJF, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'DJF '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The '\n", + " 'metric '\n", + " 'is '\n", + " 'the '\n", + " 'statistical '\n", + " 'value '\n", + " 'between '\n", + " 'the '\n", + " 'model '\n", + " 'and '\n", + " 'the '\n", + " 'observations',\n", + " 'name': 'EnsoSstMapDjf',\n", + " 'units': 'C/C '\n", + " '/ '\n", + " 'C/C'}},\n", + " 'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',\n", + " 'nyears': 142,\n", + " 'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, 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'time_period': ['1871-1-16 '\n", + " '12:0:0.0',\n", + " '2012-12-16 '\n", + " '12:0:0.0']},\n", + " 'ACCESS-CM2_r3i1p1f1': {'keyerror': None,\n", + " 'name': 'ACCESS-CM2_r3i1p1f1',\n", + " 'nyears': 165,\n", + " 'time_period': ['1850-1-16 '\n", + " '12:0:0.0',\n", + " '2014-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-20C': {'name': 'ERA-20C',\n", + " 'nyears': 111,\n", + " 'time_period': ['1900-1-16 '\n", + " '12:0:0.0',\n", + " '2010-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-5': {'name': 'ERA-5',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': ''},\n", + " 'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '\n", + " \"20CRv2's \"\n", + " 'ts; '\n", + " \"ERA-20C's \"\n", + " 'ts; '\n", + " \"ERA-5's \"\n", + " 'ts; '\n", + " \"ERA-Interim's \"\n", + " 'ts; '\n", + " \"HadISST's \"\n", + " 'ts; '\n", + " \"Tropflux's \"\n", + " 'ts; '\n", + " \"'s \",\n", + " 'method': 'The 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'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 '\n", + " '12:0:0.0',\n", + " '2018-12-16 '\n", + " '12:0:0.0']},\n", + " 'method': 'nino3.4 '\n", + " 'SSTA '\n", + " 'regressed '\n", + " 'against '\n", + " 'surface '\n", + " 'temperature '\n", + " 'anomalies '\n", + " 'in '\n", + " 'global '\n", + " 'during '\n", + " 'JJA, '\n", + " 'time '\n", + " 'series '\n", + " 'are '\n", + " 'linearly '\n", + " 'detrended, '\n", + " 'observations '\n", + " 'and '\n", + " 'model '\n", + " 'regridded '\n", + " 'to '\n", + " 'generic_1x1deg',\n", + " 'name': 'ENSO '\n", + " 'JJA '\n", + " 'TSA '\n", + " 'pattern',\n", + " 'ref': 'Using '\n", + " 'CDAT '\n", + " 'regridding, '\n", + " 'correlation '\n", + " '(centered '\n", + " 'and '\n", + " 'biased), '\n", + " 'std '\n", + " '(centered '\n", + " 'and '\n", + " 'biased) '\n", + " 'and '\n", + " 'rms '\n", + " '(uncentered '\n", + " 'and '\n", + " 'biased) '\n", + " 'calculation',\n", + " 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0.07141335043624629},\n", + " 'ERA-20C': {'value': 0.8265126323027572,\n", + " 'value_error': 0.07844910735406072},\n", + " 'ERA-5': {'value': 0.9075909980564855,\n", + " 'value_error': 0.14350273688619733},\n", + " 'ERA-Interim': {'value': 0.9001341048707652,\n", + " 'value_error': 0.1423236985494241},\n", + " 'HadISST': {'value': 0.7688706055408969,\n", + " 'value_error': 0.06298833428079066},\n", + " 'Tropflux': {'value': 0.9128364190673677,\n", + " 'value_error': 0.14617081051130443}},\n", + " 'metric': {'20CRv2': {'value': 23.829109289853704,\n", + " 'value_error': 20.031591096914156},\n", + " 'ERA-20C': {'value': 10.986941968393225,\n", + " 'value_error': 19.174736053152937},\n", + " 'ERA-5': {'value': 1.072079553416787,\n", + " 'value_error': 23.849349683580996},\n", + " 'ERA-Interim': {'value': 1.9093811257160185,\n", + " 'value_error': 24.046922525424254},\n", + " 'HadISST': {'value': 19.307603771634533,\n", + " 'value_error': 19.062144093701967},\n", + " 'Tropflux': {'value': 0.49129027000419734,\n", + " 'value_error': 23.914721121689627}}},\n", + " 'EnsoPrMapDjfCorr': {'diagnostic': {'20CRv2_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " '20CRv2_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ACCESS-CM2_r3i1p1f1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-20C_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 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'metric': {'20CRv2_CMAP': {'value': 0.2445041108775713,\n", + " 'value_error': None},\n", + " '20CRv2_ERA-Interim': {'value': 0.29747317130155093,\n", + " 'value_error': None},\n", + " '20CRv2_GPCPv2.3': {'value': 0.27179868103228366,\n", + " 'value_error': None},\n", + " '20CRv2_TRMM-3B43v-7': {'value': 0.4037432958530064,\n", + " 'value_error': None},\n", + " 'ERA-20C_CMAP': {'value': 0.2574662811663777,\n", + " 'value_error': None},\n", + " 'ERA-20C_ERA-Interim': {'value': 0.31989800780160726,\n", + " 'value_error': None},\n", + " 'ERA-20C_GPCPv2.3': {'value': 0.2826073906892712,\n", + " 'value_error': None},\n", + " 'ERA-20C_TRMM-3B43v-7': {'value': 0.44253755456537525,\n", + " 'value_error': None},\n", + " 'ERA-5_CMAP': {'value': 0.25758644904259664,\n", + " 'value_error': None},\n", + " 'ERA-5_ERA-Interim': {'value': 0.3198993458391334,\n", + " 'value_error': None},\n", + " 'ERA-5_GPCPv2.3': {'value': 0.29052494908540016,\n", + " 'value_error': None},\n", + " 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None,\n", + " 'value_error': None},\n", + " 'ERA-5_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-5_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_GPCPv2.3': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST_TRMM-3B43v-7': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_CMAP': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'Tropflux_GPCPv2.3': {'value': None,\n", + " 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" 'ERA-5_GPCPv2.3': {'value': 0.2222906017330502,\n", + " 'value_error': None},\n", + " 'ERA-5_TRMM-3B43v-7': {'value': 0.33161500605829025,\n", + " 'value_error': None},\n", + " 'ERA-Interim_CMAP': {'value': 0.21377084019473622,\n", + " 'value_error': None},\n", + " 'ERA-Interim_ERA-Interim': {'value': 0.22869535976120117,\n", + " 'value_error': None},\n", + " 'ERA-Interim_GPCPv2.3': {'value': 0.22308566256491205,\n", + " 'value_error': None},\n", + " 'ERA-Interim_TRMM-3B43v-7': {'value': 0.3264346281958339,\n", + " 'value_error': None},\n", + " 'HadISST_CMAP': {'value': 0.21749313654914765,\n", + " 'value_error': None},\n", + " 'HadISST_ERA-Interim': {'value': 0.23204094149487148,\n", + " 'value_error': None},\n", + " 'HadISST_GPCPv2.3': {'value': 0.22642331679324232,\n", + " 'value_error': None},\n", + " 'HadISST_TRMM-3B43v-7': {'value': 0.3289356731594322,\n", + " 'value_error': None},\n", + " 'Tropflux_CMAP': {'value': 0.21474054175332313,\n", + " 'value_error': None},\n", + " 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'PythonVersion': '3.7.3.final.0',\n", + " 'Version': '23.1.0',\n", + " 'buildVersion': '3.18.8'},\n", + " 'date': '2023-09-19 10:49:28',\n", + " 'openGL': {'GLX': {'client': {'vendor': 'Mesa '\n", + " 'Project '\n", + " 'and '\n", + " 'SGI',\n", + " 'version': '1.4'},\n", + " 'server': {'vendor': 'SGI',\n", + " 'version': '1.4'},\n", + " 'version': '1.4'},\n", + " 'renderer': 'llvmpipe (LLVM '\n", + " '7.0, 256 bits)',\n", + " 'shading language version': '1.20',\n", + " 'vendor': 'VMware, Inc.',\n", + " 'version': '2.1 Mesa '\n", + " '18.3.4'},\n", + " 'osAccess': False,\n", + " 'packages': {'PMP': 'v3.0.2-11-g06b151f',\n", + " 'PMPObs': 'See '\n", + " \"'References' \"\n", + " 'key below, for '\n", + " 'detailed obs '\n", + " 'provenance '\n", + " 'information.',\n", + " 'blas': '0.3.23',\n", + " 'cdat_info': '8.2.1',\n", + " 'cdms': '3.1.5',\n", + " 'cdp': '1.7.0',\n", + " 'cdtime': '3.1.4',\n", + " 'cdutil': '8.2.1',\n", + " 'clapack': None,\n", + " 'esmf': '8.4.2',\n", + " 'esmpy': '8.4.2',\n", + " 'genutil': '8.2.1',\n", + " 'lapack': '3.9.0',\n", + " 'matplotlib': '3.7.1',\n", + " 'mesalib': None,\n", + " 'numpy': '1.23.5',\n", + " 'python': '3.10.10',\n", + " 'scipy': '1.11.2',\n", + " 'uvcdat': None,\n", + " 'vcs': None,\n", + " 'vtk': None,\n", + " 'xarray': '2023.8.0',\n", + " 'xcdat': '0.5.0'},\n", + " 'platform': {'Name': 'gates.llnl.gov',\n", + " 'OS': 'Linux',\n", + " 'Version': '3.10.0-1160.71.1.el7.x86_64'},\n", + " 'userId': 'lee1043'}}},\n", + " 'mjo': {'REFERENCE': {'GPCP-1-3': {'MJJASO': {'analysis_time_window_end_year': 2010,\n", + " 'analysis_time_window_start_year': 1997,\n", + " 'east_power': 0.017083859661554807,\n", + " 'east_west_power_ratio': 3.0534932772936827,\n", + " 'west_power': 0.005594857466559176},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2010,\n", + " 'analysis_time_window_start_year': 1997,\n", + " 'east_power': 0.0212067408339334,\n", + " 'east_west_power_ratio': 2.494024187737836,\n", + " 'west_power': 0.008503021317194452}}},\n", + " 'RESULTS': {'ACCESS-CM2': {'r10i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.008454461395462963,\n", + " 'east_west_power_ratio': 1.9523393478587807,\n", + " 'west_power': 0.004330426165274677},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.010728533372104533,\n", + " 'east_west_power_ratio': 2.036111337054465,\n", + " 'west_power': 0.005269129038702244}},\n", + " 'r1i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.008322433128973817,\n", + " 'east_power_normalized_by_observation': 0.4871518084231552,\n", + " 'east_west_power_ratio': 2.0540771945510765,\n", + " 'west_power': 0.004051665220299914},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.00892745702168386,\n", + " 'east_power_normalized_by_observation': 0.42097260920918256,\n", + " 'east_west_power_ratio': 1.494895047416935,\n", + " 'west_power': 0.005971962404390747}},\n", + " 'r2i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.0097250199770383,\n", + " 'east_west_power_ratio': 2.157133934815046,\n", + " 'west_power': 0.004508306053732417},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009400282118275849,\n", + " 'east_west_power_ratio': 1.747882443125181,\n", + " 'west_power': 0.005378097454579566}},\n", + " 'r3i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.00885718789254206,\n", + " 'east_west_power_ratio': 1.9404895204133192,\n", + " 'west_power': 0.004564409031518759},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.008617001858747617,\n", + " 'east_west_power_ratio': 1.8477165407824487,\n", + " 'west_power': 0.004663595128665457}},\n", + " 'r4i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.00944821996671019,\n", + " 'east_west_power_ratio': 2.2920015976364865,\n", + " 'west_power': 0.004122257146964121},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.007664235940513062,\n", + " 'east_west_power_ratio': 1.3908972436814913,\n", + " 'west_power': 0.005510281924369199}},\n", + " 'r5i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009441837107564724,\n", + " 'east_west_power_ratio': 1.9386484006636087,\n", + " 'west_power': 0.004870319499055495},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009658805004219548,\n", + " 'east_west_power_ratio': 1.6985195462238116,\n", + " 'west_power': 0.005686602209372997}},\n", + " 'r6i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.00822954828605968,\n", + " 'east_west_power_ratio': 1.8957916349047121,\n", + " 'west_power': 0.004340956112760419},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.008723318546158937,\n", + " 'east_west_power_ratio': 1.5377174840020025,\n", + " 'west_power': 0.005672900670580901}},\n", + " 'r7i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.007753277717156017,\n", + " 'east_west_power_ratio': 1.699071538624335,\n", + " 'west_power': 0.004563243831059351},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.008959871265524876,\n", + " 'east_west_power_ratio': 1.3568240168350207,\n", + " 'west_power': 0.006603561813731018}},\n", + " 'r8i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009022245708770974,\n", + " 'east_west_power_ratio': 1.8232971544330576,\n", + " 'west_power': 0.004948313382069848},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.00848233457926388,\n", + " 'east_west_power_ratio': 1.5024424355768744,\n", + " 'west_power': 0.005645696885556232}},\n", + " 'r9i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009572896775638086,\n", + " 'east_west_power_ratio': 2.3374986903501314,\n", + " 'west_power': 0.004095359203903627},\n", + " 'NDJFMA': {'analysis_time_window_end_year': 2004,\n", + " 'analysis_time_window_start_year': 1985,\n", + " 'east_power': 0.009897550247832572,\n", + " 'east_west_power_ratio': 1.7529533958869599,\n", + " 'west_power': 0.005646214138411025}}}},\n", + " 'provenance': {'commandLine': '/home/lee1043/.conda/envs/pcmdi_metrics_dev_20230822/bin/mjo_metrics_driver.py '\n", + " '-p ../param/myParam_mjo.py --case_id '\n", + " 'v20230924 --mip cmip6 --modnames '\n", + " 'ACCESS-CM2 --realization r1i1p1f1 '\n", + " '--parallel',\n", + " 'conda': {'Platform': 'linux-64',\n", + " 'PythonVersion': '3.10.12.final.0',\n", + " 'Version': '23.3.1',\n", + " 'buildVersion': 'not installed'},\n", + " 'date': '2023-09-24 13:35:17',\n", + " 'openGL': {'GLX': {'client': {'vendor': 'Mesa Project '\n", + " 'and SGI',\n", + " 'version': '1.4'},\n", + " 'server': {'vendor': 'SGI',\n", + " 'version': '1.4'},\n", + " 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'\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' '\n", + " 'if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if '\n", + " 'there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' '\n", + " 'if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if '\n", + " '\"REF\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to '\n", + " 'keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for '\n", + " 'json '\n", + " 'archive\\n'\n", + " ' '\n", + " 'if '\n", + " '\"obs\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"source\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '-\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'North '\n", + " 'test '\n", + " '-- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- '\n", + " '\", '\n", + " 'model, '\n", + " '\" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'model '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run '\n", + " 'can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " 'for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- '\n", + " '\", '\n", + " 'run, '\n", + " '\" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'run '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 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'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = '\n", + " '{}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " '\"cbf\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid '\n", + " 'to 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'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of '\n", + " 'cbf '\n", + " 'pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 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'model_timeseries_season_subdomain, '\n", + " '# '\n", + " 'native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' 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\"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n '\n", + " 'in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs '\n", + " '= '\n", + " '\"eof\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ 1)\\n'\n", + " ' '\n", + " 'if '\n", + " 'eofs '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for '\n", + " 'each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac '\n", + " '= '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF '\n", + " 'PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model '\n", + " 'PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'swap '\n", + " 'diagnosis\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list.append(dict_head[\"rms\"])\\n'\n", + " ' '\n", + " 'cor_list.append(dict_head[\"cor\"])\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tcor_list.append(dict_head[\"tcor_cbf_vs_eof_pc\"])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Find '\n", + " 'best '\n", + " 'matching '\n", + " 'eofs '\n", + " 'with '\n", + " 'different '\n", + " 'criteria\\n'\n", + " ' '\n", + " 'best_matching_eofs_rms '\n", + " '= '\n", + " 'rms_list.index(min(rms_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'best_matching_eofs_cor '\n", + " '= '\n", + " 'cor_list.index(max(cor_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'best_matching_eofs_tcor '\n", + " '= '\n", + " 'tcor_list.index(max(tcor_list)) '\n", + " '+ 1\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'the '\n", + " 'best '\n", + " 'matching '\n", + " 'information '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season]\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__rms\"] '\n", + " '= '\n", + " 'best_matching_eofs_rms\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__cor\"] '\n", + " '= '\n", + " 'best_matching_eofs_cor\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'dict_head[\\n'\n", + " ' '\n", + " '\"best_matching_model_eofs__tcor_cbf_vs_eof_pc\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'best_matching_eofs_tcor\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'eof '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '=================================================================\\n'\n", + " ' '\n", + " '# '\n", + " 'Dictionary '\n", + " 'to '\n", + " 'JSON: '\n", + " 'individual '\n", + " 'JSON '\n", + " 'during '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " ' '\n", + " '# '\n", + " '-----------------------------------------------------------------\\n'\n", + " ' '\n", + " 'json_filename_tmp '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'variability_metrics_to_json(\\n'\n", + " ' '\n", + " 'outdir,\\n'\n", + " ' '\n", + " 'json_filename_tmp,\\n'\n", + " ' '\n", + " 'result_dict,\\n'\n", + " ' '\n", + " 'model=model,\\n'\n", + " ' '\n", + " 'run=run,\\n'\n", + " ' '\n", + " 'cmec_flag=cmec,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'except '\n", + " 'Exception '\n", + " 'as '\n", + " 'err:\\n'\n", + " ' '\n", + " 'if '\n", + " 'debug:\\n'\n", + " ' '\n", + " 'raise\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'print(\"warning: '\n", + " 'failed '\n", + " 'for '\n", + " '\", '\n", + " 'model, '\n", + " 'run, '\n", + " 'err)\\n'\n", + " ' '\n", + " 'pass\\n'\n", + " '\\n'\n", + " '# 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" 'model_index_path '\n", + " '= '\n", + " 'param.modpath.split(\"/\")[-1].split(\".\").index(\"%(model)\")\\n'\n", + " ' '\n", + " 'models '\n", + " '= [\\n'\n", + " ' '\n", + " 'p.split(\"/\")[-1].split(\".\")[model_index_path]\\n'\n", + " ' '\n", + " 'for p '\n", + " 'in '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=\"*\", '\n", + " 'realization=\"*\", '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' # '\n", + " 'remove '\n", + " 'duplicates\\n'\n", + " ' '\n", + " 'models '\n", + " '= '\n", + " 'sorted(list(dict.fromkeys(models)), '\n", + " 'key=lambda '\n", + " 's: '\n", + " 's.lower())\\n'\n", + " '\\n'\n", + " 'print(\"models:\", '\n", + " 'models)\\n'\n", + " 'print(\"number '\n", + " 'of '\n", + " 'models:\", '\n", + " 'len(models))\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Realizations\\n'\n", + " 'realization '\n", + " '= '\n", + " 'param.realization\\n'\n", + " 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" ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Debug\\n'\n", + " 'debug '\n", + " '= '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Year\\n'\n", + " 'msyear '\n", + " '= '\n", + " 'param.msyear\\n'\n", + " 'meyear '\n", + " '= '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear '\n", + " '= '\n", + " 'param.osyear\\n'\n", + " 'oeyear '\n", + " '= '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# '\n", + " 'lon1g '\n", + " 'and '\n", + " 'lon2g '\n", + " 'is '\n", + " 'for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if '\n", + " 'mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= 0\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '360\\n'\n", + " 'else:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= '\n", + " '-180\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' '\n", + " 'if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if '\n", + " 'there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' '\n", + " 'if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if '\n", + " '\"REF\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to '\n", + " 'keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for '\n", + " 'json '\n", + " 'archive\\n'\n", + " ' '\n", + " 'if '\n", + " '\"obs\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"source\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '-\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'North '\n", + " 'test '\n", + " '-- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- '\n", + " '\", '\n", + " 'model, '\n", + " '\" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'model '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run '\n", + " 'can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " 'for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- '\n", + " '\", '\n", + " 'run, '\n", + " '\" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'run '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: '\n", + " '\" + '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = '\n", + " '{}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " '\"cbf\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid '\n", + " 'to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to '\n", + " 'that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'axes '\n", + " 'for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes '\n", + " '= '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid '\n", + " 'to 0, '\n", + " 'so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect '\n", + " 'to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) '\n", + " 'Give '\n", + " \"obs's \"\n", + " 'mask '\n", + " 'to '\n", + " 'model '\n", + " 'field, '\n", + " 'so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF '\n", + " 'PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '= '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of '\n", + " 'cbf '\n", + " 'pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# '\n", + " 'cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# '\n", + " 'native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"cbf\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n '\n", + " 'in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs '\n", + " '= '\n", + " '\"eof\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ 1)\\n'\n", + " ' '\n", + " 'if '\n", + " 'eofs '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for '\n", + " 'each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac '\n", + " '= '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF '\n", + " 'PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model '\n", + " 'PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' 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'\n", + " 'param.plot_obs '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'nc_out_model '\n", + " '= '\n", + " 'param.nc_out '\n", + " '# Record '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " 'plot_model '\n", + " '= '\n", + " 'param.plot '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'update_json '\n", + " '= '\n", + " 'param.update_json\\n'\n", + " '\\n'\n", + " 'print(\"nc_out_obs, '\n", + " 'plot_obs:\", '\n", + " 'nc_out_obs, '\n", + " 'plot_obs)\\n'\n", + " 'print(\"nc_out_model, '\n", + " 'plot_model:\", '\n", + " 'nc_out_model, '\n", + " 'plot_model)\\n'\n", + " '\\n'\n", + " 'cmec = '\n", + " 'False\\n'\n", + " 'if '\n", + " 'hasattr(param, '\n", + " '\"cmec\"):\\n'\n", + " ' cmec '\n", + " '= '\n", + " 'param.cmec '\n", + " '# '\n", + " 'Generate '\n", + " 'CMEC '\n", + " 'compliant '\n", + " 'json\\n'\n", + " 'print(\"CMEC:\" '\n", + " '+ '\n", + " 'str(cmec))\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'given '\n", + " 'mode of '\n", + " 'variability\\n'\n", + " 'mode = '\n", + " 'VariabilityModeCheck(param.variability_mode, '\n", + " 'P)\\n'\n", + " 'print(\"mode:\", '\n", + " 'mode)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Variables\\n'\n", + " 'var = '\n", + " 'param.varModel\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'dependency '\n", + " 'for '\n", + " 'given '\n", + " 'season '\n", + " 'option\\n'\n", + " 'seasons '\n", + " '= '\n", + " 'param.seasons\\n'\n", + " 'print(\"seasons:\", '\n", + " 'seasons)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Observation '\n", + " 'information\\n'\n", + " 'obs_name '\n", + " '= '\n", + " 'param.reference_data_name\\n'\n", + " 'obs_path '\n", + " '= '\n", + " 'param.reference_data_path\\n'\n", + " 'obs_var '\n", + " '= '\n", + " 'param.varOBS\\n'\n", + " '\\n'\n", + " '# Path '\n", + " 'to model '\n", + " 'data as '\n", + " 'string '\n", + " 'template\\n'\n", + " 'modpath '\n", + " '= '\n", + " 'StringConstructor(param.modpath)\\n'\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'modpath_lf '\n", + " '= '\n", + " 'StringConstructor(param.modpath_lf)\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'given '\n", + " 'model '\n", + " 'option\\n'\n", + " 'models = '\n", + " 'param.modnames\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Include '\n", + " 'all '\n", + " 'models '\n", + " 'if '\n", + " 'conditioned\\n'\n", + " 'if '\n", + " '(\"all\" '\n", + " 'in '\n", + " '[m.lower() '\n", + " 'for m in '\n", + " 'models]) '\n", + " 'or '\n", + " '(models '\n", + " '== '\n", + " '\"all\"):\\n'\n", + " ' '\n", + " 'model_index_path '\n", + " '= '\n", + " 'param.modpath.split(\"/\")[-1].split(\".\").index(\"%(model)\")\\n'\n", + " ' '\n", + " 'models = '\n", + " '[\\n'\n", + " ' '\n", + " 'p.split(\"/\")[-1].split(\".\")[model_index_path]\\n'\n", + " ' '\n", + " 'for p in '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=\"*\", '\n", + " 'realization=\"*\", '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' ]\\n'\n", + " ' # '\n", + " 'remove '\n", + " 'duplicates\\n'\n", + " ' '\n", + " 'models = '\n", + " 'sorted(list(dict.fromkeys(models)), '\n", + " 'key=lambda '\n", + " 's: '\n", + " 's.lower())\\n'\n", + " '\\n'\n", + " 'print(\"models:\", '\n", + " 'models)\\n'\n", + " 'print(\"number '\n", + " 'of '\n", + " 'models:\", '\n", + " 'len(models))\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Realizations\\n'\n", + " 'realization '\n", + " '= '\n", + " 'param.realization\\n'\n", + " 'print(\"realization: '\n", + " '\", '\n", + " 'realization)\\n'\n", + " '\\n'\n", + " '# EOF '\n", + " 'ordinal '\n", + " 'number\\n'\n", + " 'eofn_obs '\n", + " '= '\n", + " 'int(param.eofn_obs)\\n'\n", + " 'eofn_mod '\n", + " '= '\n", + " 'int(param.eofn_mod)\\n'\n", + " '\\n'\n", + " '# case '\n", + " 'id\\n'\n", + " 'case_id '\n", + " '= '\n", + " 'param.case_id\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Output\\n'\n", + " 'outdir_template '\n", + " '= '\n", + " 'param.process_templated_argument(\"results_dir\")\\n'\n", + " 'outdir = '\n", + " 'StringConstructor(\\n'\n", + " ' '\n", + " 'str(\\n'\n", + " ' '\n", + " 'outdir_template(\\n'\n", + " ' '\n", + " 'output_type=\"%(output_type)\",\\n'\n", + " ' '\n", + " 'mip=mip,\\n'\n", + " ' '\n", + " 'exp=exp,\\n'\n", + " ' '\n", + " 'variability_mode=mode,\\n'\n", + " ' '\n", + " 'reference_data_name=obs_name,\\n'\n", + " ' '\n", + " 'case_id=case_id,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' )\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# Debug\\n'\n", + " 'debug = '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# Year\\n'\n", + " 'msyear = '\n", + " 'param.msyear\\n'\n", + " 'meyear = '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear = '\n", + " 'param.osyear\\n'\n", + " 'oeyear = '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# lon1g '\n", + " 'and '\n", + " 'lon2g is '\n", + " 'for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' '\n", + " 'lon1g = '\n", + " '0\\n'\n", + " ' '\n", + " 'lon2g = '\n", + " '360\\n'\n", + " 'else:\\n'\n", + " ' '\n", + " 'lon1g = '\n", + " '-180\\n'\n", + " ' '\n", + " 'lon2g = '\n", + " '180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, 0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, 31, '\n", + " '23, 59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, 0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, 31, '\n", + " '23, 59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' [\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' ]\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if \"REF\" '\n", + " 'not in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear = '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs = '\n", + " '{}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for json '\n", + " 'archive\\n'\n", + " ' if '\n", + " '\"obs\" '\n", + " 'not in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " '\"defaultReference\" '\n", + " 'not in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " '\"source\" '\n", + " 'not in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " 'mode not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" + '\n", + " 'str(oeyear)\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - -\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean (if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . . '\n", + " '. . . . '\n", + " '. . . . '\n", + " '. . . . '\n", + " '. . . . '\n", + " '. . . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" + '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] \" '\n", + " '+ '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] \" '\n", + " '+ '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Save '\n", + " 'stdv of '\n", + " 'PC time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# North '\n", + " 'test -- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- \", '\n", + " 'model, \" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' if '\n", + " 'model '\n", + " 'not in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' if '\n", + " 'realization '\n", + " '== \"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== \"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- \", '\n", + " 'run, \" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if run '\n", + " 'not in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if mode '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# read '\n", + " 'data in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear = '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: '\n", + " '\" + '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean (if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if \"cbf\" '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# Save '\n", + " 'axes for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes = '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid to '\n", + " '0, so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect '\n", + " 'to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) '\n", + " 'Give '\n", + " \"obs's \"\n", + " 'mask to '\n", + " 'model '\n", + " 'field, '\n", + " 'so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc = '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'cbf pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# '\n", + " 'cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc / '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by cbf '\n", + " 'pc (on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc / '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"cbf\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name for '\n", + " 'NetCDF '\n", + " 'and plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- plot '\n", + " 'map '\n", + " 'image to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" + '\n", + " 'model + '\n", + " '\" (\" + '\n", + " 'run + '\n", + " '\")\" + \" '\n", + " '- CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" + '\n", + " 'model + '\n", + " '\" (\" + '\n", + " 'run + '\n", + " '\")\" + \" '\n", + " '- CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs = '\n", + " '\"eof\" + '\n", + " 'str(n + '\n", + " '1)\\n'\n", + " ' '\n", + " 'if eofs '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac = '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr = '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr = '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name for '\n", + " 'NetCDF '\n", + " 'and plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(n + '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- plot '\n", + " 'map '\n", + " 'image to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" \"\\n'\n", + " ' '\n", + " '+ model\\n'\n", + " ' '\n", + " '+ \" (\"\\n'\n", + " ' '\n", + " '+ run\\n'\n", + " ' '\n", + " '+ \") - '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ str(n '\n", + " '+ 1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" \"\\n'\n", + " ' '\n", + " '+ model\\n'\n", + " ' '\n", + " '+ \" (\"\\n'\n", + " ' '\n", + " '+ run\\n'\n", + " ' '\n", + " '+ \") - '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ str(n '\n", + " '+ 1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'swap '\n", + " 'diagnosis\\n'\n", + " ' '\n", + " '# - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- - - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list.append(dict_head[\"rms\"])\\n'\n", + " ' '\n", + " 'cor_list.append(dict_head[\"cor\"])\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " 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'print(\"realization: '\n", + " '\", '\n", + " 'realization)\\n'\n", + " '\\n'\n", + " '# EOF '\n", + " 'ordinal '\n", + " 'number\\n'\n", + " 'eofn_obs '\n", + " '= '\n", + " 'int(param.eofn_obs)\\n'\n", + " 'eofn_mod '\n", + " '= '\n", + " 'int(param.eofn_mod)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'case '\n", + " 'id\\n'\n", + " 'case_id '\n", + " '= '\n", + " 'param.case_id\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Output\\n'\n", + " 'outdir_template '\n", + " '= '\n", + " 'param.process_templated_argument(\"results_dir\")\\n'\n", + " 'outdir '\n", + " '= '\n", + " 'StringConstructor(\\n'\n", + " ' '\n", + " 'str(\\n'\n", + " ' '\n", + " 'outdir_template(\\n'\n", + " ' '\n", + " 'output_type=\"%(output_type)\",\\n'\n", + " ' '\n", + " 'mip=mip,\\n'\n", + " ' '\n", + " 'exp=exp,\\n'\n", + " ' '\n", + " 'variability_mode=mode,\\n'\n", + " ' '\n", + " 'reference_data_name=obs_name,\\n'\n", + " ' '\n", + " 'case_id=case_id,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " ')\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Debug\\n'\n", + " 'debug '\n", + " '= '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Year\\n'\n", + " 'msyear '\n", + " '= '\n", + " 'param.msyear\\n'\n", + " 'meyear '\n", + " '= '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear '\n", + " '= '\n", + " 'param.osyear\\n'\n", + " 'oeyear '\n", + " '= '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# '\n", + " 'lon1g '\n", + " 'and '\n", + " 'lon2g '\n", + " 'is '\n", + " 'for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if '\n", + " 'mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= 0\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '360\\n'\n", + " 'else:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= '\n", + " '-180\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' '\n", + " 'if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if '\n", + " 'there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' '\n", + " 'if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if '\n", + " '\"REF\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to '\n", + " 'keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for '\n", + " 'json '\n", + " 'archive\\n'\n", + " ' '\n", + " 'if '\n", + " '\"obs\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"source\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '-\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'North '\n", + " 'test '\n", + " '-- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- '\n", + " '\", '\n", + " 'model, '\n", + " '\" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'model '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run '\n", + " 'can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " 'for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- '\n", + " '\", '\n", + " 'run, '\n", + " '\" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'run '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: '\n", + " '\" + '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = '\n", + " '{}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " '\"cbf\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid '\n", + " 'to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to '\n", + " 'that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'axes '\n", + " 'for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes '\n", + " '= '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid '\n", + " 'to 0, '\n", + " 'so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect '\n", + " 'to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) '\n", + " 'Give '\n", + " \"obs's \"\n", + " 'mask '\n", + " 'to '\n", + " 'model '\n", + " 'field, '\n", + " 'so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF '\n", + " 'PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '= '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of '\n", + " 'cbf '\n", + " 'pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# '\n", + " 'cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# '\n", + " 'native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' 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\"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n '\n", + " 'in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs '\n", + " '= '\n", + " '\"eof\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ 1)\\n'\n", + " ' '\n", + " 'if '\n", + " 'eofs '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for '\n", + " 'each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac '\n", + " '= '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF '\n", + " 'PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model '\n", + " 'PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + 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'rmsc_glo': 1.1328712984178666,\n", + " 'stdv_pc': 0.18850311528607333,\n", + " 'stdv_pc_ratio_to_obs': 0.7979417507206015,\n", + " 'tcor_cbf_vs_eof_pc': -0.14715348032728637},\n", + " 'period': '1900-2005'},\n", + " 'target_model_eofs': 1}}},\n", + " 'r5i1p1f1': {'defaultReference': {'PDO': {'monthly': {'best_matching_model_eofs__cor': 1,\n", + " 'best_matching_model_eofs__rms': 1,\n", + " 'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,\n", + " 'cbf': {'bias': 0.006268492101318119,\n", + " 'bias_glo': -0.0068069594606150335,\n", + " 'cor': 0.9074051855009615,\n", + " 'cor_glo': 0.7591511151631094,\n", + " 'frac': 0.16847140618022965,\n", + " 'frac_cbf_regrid': 0.16719798810738276,\n", + " 'mean': -8.835341426015429e-18,\n", + " 'mean_glo': 0.06457000201010714,\n", + " 'rms': 0.13923952948002133,\n", + " 'rms_glo': 0.10373229685734998,\n", + " 'rmsc': 0.4433674279242072,\n", + " 'rmsc_glo': 0.7067000062757975,\n", + " 'stdv_pc': 0.24005780611348693,\n", + " 'stdv_pc_ratio_to_obs': 1.0161749623800218},\n", + " 'eof1': {'bias': 0.005676476800611998,\n", + " 'bias_glo': -0.032278905874797564,\n", + " 'cor': 0.7557461946533934,\n", + " 'cor_glo': 0.6253984002350329,\n", + " 'frac': 0.1907282775048135,\n", + " 'mean': -7.440287516644572e-18,\n", + " 'mean_glo': 0.03906115263270226,\n", + " 'rms': 0.2317392281837409,\n", + " 'rms_glo': 0.1321303293333717,\n", + " 'rmsc': 0.7186619096438679,\n", + " 'rmsc_glo': 0.8814205181256387,\n", + " 'stdv_pc': 0.2867805716707198,\n", + " 'stdv_pc_ratio_to_obs': 1.2139544276725034,\n", + " 'tcor_cbf_vs_eof_pc': 0.8830254225832019},\n", + " 'eof2': {'bias': 0.006090925243876879,\n", + " 'bias_glo': -0.0022938372660884637,\n", + " 'cor': 0.39443979820169206,\n", + " 'cor_glo': 0.3440049270224594,\n", + " 'frac': 0.10870538581190156,\n", + " 'mean': -8.060311476364952e-18,\n", + " 'mean_glo': 0.06861075278512258,\n", + " 'rms': 0.3100486388438745,\n", + " 'rms_glo': 0.1574133844954579,\n", + " 'rmsc': 1.129379836513501,\n", + " 'rmsc_glo': 1.168191414142415,\n", + " 'stdv_pc': 0.21650489193928782,\n", + " 'stdv_pc_ratio_to_obs': 0.9164744691430222,\n", + " 'tcor_cbf_vs_eof_pc': 0.3510341985861876},\n", + " 'eof3': {'bias': 0.005772503814225008,\n", + " 'bias_glo': -0.012602803428885728,\n", + " 'cor': 0.2778617445508622,\n", + " 'cor_glo': 0.45102680772030856,\n", + " 'frac': 0.07925655937199633,\n", + " 'mean': 8.060311476364952e-18,\n", + " 'mean_glo': -0.058583669214479786,\n", + " 'rms': 0.31908701099534265,\n", + " 'rms_glo': 0.1447755314138477,\n", + " 'rmsc': 1.235076516784705,\n", + " 'rmsc_glo': 1.067050726245745,\n", + " 'stdv_pc': 0.18486716890556057,\n", + " 'stdv_pc_ratio_to_obs': 0.782550634154758,\n", + " 'tcor_cbf_vs_eof_pc': -0.2118221098635416},\n", + " 'period': '1900-2005'},\n", + " 'target_model_eofs': 1}}}}},\n", + " 'provenance': {'commandLine': '../variability_modes_driver.py '\n", + " '-p '\n", + " '../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_PDO_cmip6.py '\n", + " '--case_id '\n", + " 'v20220825 '\n", + " '--mip '\n", + " 'cmip6 '\n", + " '--exp '\n", + " 'historical '\n", + " '--modnames '\n", + " 'UKESM1-0-LL '\n", + " '--realization '\n", + " 'r9i1p1f2 '\n", + " '--parallel '\n", + " 'True '\n", + " '--no_nc_out_obs '\n", + " '--no_plot_obs',\n", + " 'conda': {'Platform': 'linux-64',\n", + " 'PythonVersion': '3.7.3.final.0',\n", + " 'Version': '4.14.0',\n", + " 'buildVersion': '3.18.8'},\n", + " 'date': '2022-08-25 '\n", + " '21:55:11',\n", + " 'history': '',\n", + " 'openGL': {'GLX': {'client': {},\n", + " 'server': {}}},\n", + " 'osAccess': False,\n", + " 'packages': {'PMP': '2.0',\n", + " 'PMPObs': 'See '\n", + " \"'References' \"\n", + " 'key '\n", + " 'below, '\n", + " 'for '\n", + " 'detailed '\n", + " 'obs '\n", + " 'provenance '\n", + " 'information.',\n", + " 'blas': '0.3.21',\n", + " 'cdat_info': '8.2.1',\n", + " 'cdms': '3.1.5',\n", + " 'cdp': '1.7.0',\n", + " 'cdtime': '3.1.4',\n", + " 'cdutil': '8.2.1',\n", + " 'clapack': None,\n", + " 'esmf': '8.2.0',\n", + " 'esmpy': '8.2.0',\n", + " 'genutil': '8.2.1',\n", + " 'lapack': '3.9.0',\n", + " 'matplotlib': None,\n", + " 'mesalib': None,\n", + " 'numpy': '1.23.2',\n", + " 'python': '3.10.6',\n", + " 'scipy': '1.9.0',\n", + " 'uvcdat': None,\n", + " 'vcs': None,\n", + " 'vtk': None},\n", + " 'platform': {'Name': 'gates.llnl.gov',\n", + " 'OS': 'Linux',\n", + " 'Version': '3.10.0-1160.71.1.el7.x86_64'},\n", + " 'script': '#!/usr/bin/env '\n", + " 'python\\n'\n", + " '\\n'\n", + " '\"\"\"\\n'\n", + " '# Modes '\n", + " 'of '\n", + " 'Variability '\n", + " 'Metrics\\n'\n", + " '- '\n", + " 'Calculate '\n", + " 'metrics '\n", + " 'for modes '\n", + " 'of '\n", + " 'varibility '\n", + " 'from '\n", + " 'archive '\n", + " 'of CMIP '\n", + " 'models\\n'\n", + " '- Author: '\n", + " 'Jiwoo Lee '\n", + " '(lee1043@llnl.gov), '\n", + " 'PCMDI, '\n", + " 'LLNL\\n'\n", + " '\\n'\n", + " '## EOF1 '\n", + " 'based '\n", + " 'variability '\n", + " 'modes\\n'\n", + " '- NAM: '\n", + " 'Northern '\n", + " 'Annular '\n", + " 'Mode\\n'\n", + " '- NAO: '\n", + " 'Northern '\n", + " 'Atlantic '\n", + " 'Oscillation\\n'\n", + " '- SAM: '\n", + " 'Southern '\n", + " 'Annular '\n", + " 'Mode\\n'\n", + " '- PNA: '\n", + " 'Pacific '\n", + " 'North '\n", + " 'American '\n", + " 'Pattern\\n'\n", + " '- PDO: '\n", + " 'Pacific '\n", + " 'Decadal '\n", + " 'Oscillation\\n'\n", + " '\\n'\n", + " '## EOF2 '\n", + " 'based '\n", + " 'variability '\n", + " 'modes\\n'\n", + " '- NPO: '\n", + " 'North '\n", + " 'Pacific '\n", + " 'Oscillation '\n", + " '(2nd EOFs '\n", + " 'of PNA '\n", + " 'domain)\\n'\n", + " '- NPGO: '\n", + " 'North '\n", + " 'Pacific '\n", + " 'Gyre '\n", + " 'Oscillation '\n", + " '(2nd EOFs '\n", + " 'of PDO '\n", + " 'domain)\\n'\n", + " '\\n'\n", + " '## '\n", + " 'Reference:\\n'\n", + " 'Lee, J., '\n", + " 'K. 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'\n", + " 'Neither '\n", + " 'the '\n", + " 'United '\n", + " 'States '\n", + " 'government\\n'\n", + " 'nor '\n", + " 'Lawrence '\n", + " 'Livermore '\n", + " 'National '\n", + " 'Security, '\n", + " 'LLC, nor '\n", + " 'any of '\n", + " 'their '\n", + " 'employees\\n'\n", + " 'makes any '\n", + " 'warranty, '\n", + " 'expressed '\n", + " 'or '\n", + " 'implied, '\n", + " 'or '\n", + " 'assumes '\n", + " 'any legal '\n", + " 'liability '\n", + " 'or\\n'\n", + " 'responsibility '\n", + " 'for the '\n", + " 'accuracy, '\n", + " 'completeness, '\n", + " 'or '\n", + " 'usefulness '\n", + " 'of any\\n'\n", + " 'information, '\n", + " 'apparatus, '\n", + " 'product, '\n", + " 'or '\n", + " 'process '\n", + " 'disclosed, '\n", + " 'or '\n", + " 'represents '\n", + " 'that its\\n'\n", + " 'use would '\n", + " 'not '\n", + " 'infringe '\n", + " 'privately '\n", + " 'owned '\n", + " 'rights. 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The '\n", + " 'views and '\n", + " 'opinions '\n", + " 'of '\n", + " 'authors '\n", + " 'expressed\\n'\n", + " 'herein do '\n", + " 'not '\n", + " 'necessarily '\n", + " 'state or '\n", + " 'reflect '\n", + " 'those of '\n", + " 'the '\n", + " 'United '\n", + " 'States\\n'\n", + " 'government '\n", + " 'or '\n", + " 'Lawrence '\n", + " 'Livermore '\n", + " 'National '\n", + " 'Security, '\n", + " 'LLC, and '\n", + " 'shall not '\n", + " 'be used\\n'\n", + " 'for '\n", + " 'advertising '\n", + " 'or '\n", + " 'product '\n", + " 'endorsement '\n", + " 'purposes.\\n'\n", + " '\"\"\"\\n'\n", + " '\\n'\n", + " 'from '\n", + " '__future__ '\n", + " 'import '\n", + " 'print_function\\n'\n", + " '\\n'\n", + " 'import '\n", + " 'glob\\n'\n", + " 'import '\n", + " 'json\\n'\n", + " 'import '\n", + " 'os\\n'\n", + " 'import '\n", + " 'sys\\n'\n", + " 'from '\n", + " 'argparse '\n", + " 'import '\n", + " 'RawTextHelpFormatter\\n'\n", + " 'from '\n", + " 'shutil '\n", + " 'import '\n", + " 'copyfile\\n'\n", + " '\\n'\n", + " 'import '\n", + " 'cdtime\\n'\n", + " 'import '\n", + " 'cdutil\\n'\n", + " 'import '\n", + " 'MV2\\n'\n", + " 'from '\n", + " 'genutil '\n", + " 'import '\n", + " 'StringConstructor\\n'\n", + " '\\n'\n", + " 'import '\n", + " 'pcmdi_metrics\\n'\n", + " 'from '\n", + " 'pcmdi_metrics '\n", + " 'import '\n", + " 'resources\\n'\n", + " 'from '\n", + " 'pcmdi_metrics.variability_mode.lib '\n", + " 'import (\\n'\n", + " ' '\n", + " 'AddParserArgument,\\n'\n", + " ' '\n", + " 'VariabilityModeCheck,\\n'\n", + " ' '\n", + " 'YearCheck,\\n'\n", + " ' '\n", + " 'adjust_timeseries,\\n'\n", + " ' '\n", + " 'calc_stats_save_dict,\\n'\n", + " ' '\n", + " 'calcSTD,\\n'\n", + " ' '\n", + " 'calcTCOR,\\n'\n", + " ' '\n", + " 'debug_print,\\n'\n", + " ' '\n", + " 'eof_analysis_get_variance_mode,\\n'\n", + " ' '\n", + " 'gain_pcs_fraction,\\n'\n", + " ' '\n", + " 'gain_pseudo_pcs,\\n'\n", + " ' '\n", + " 'get_domain_range,\\n'\n", + " ' '\n", + " 'linear_regression_on_globe_for_teleconnection,\\n'\n", + " ' '\n", + " 'plot_map,\\n'\n", + " ' '\n", + " 'read_data_in,\\n'\n", + " ' '\n", + " 'sort_human,\\n'\n", + " ' '\n", + " 'tree,\\n'\n", + " ' '\n", + " 'variability_metrics_to_json,\\n'\n", + " ' '\n", + " 'write_nc_output,\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# To '\n", + " 'avoid '\n", + " 'below '\n", + " 'error\\n'\n", + " '# '\n", + " 'OpenBLAS '\n", + " 'blas_thread_init: '\n", + " 'pthread_create '\n", + " 'failed '\n", + " 'for '\n", + " 'thread XX '\n", + " 'of 96: '\n", + " 'Resource '\n", + " 'temporarily '\n", + " 'unavailable\\n'\n", + " 'os.environ[\"OPENBLAS_NUM_THREADS\"] '\n", + " '= \"1\"\\n'\n", + " '\\n'\n", + " '# Must be '\n", + " 'done '\n", + " 'before '\n", + " 'any CDAT '\n", + " 'library '\n", + " 'is '\n", + " 'called.\\n'\n", + " '# '\n", + " 'https://github.com/CDAT/cdat/issues/2213\\n'\n", + " 'if '\n", + " '\"UVCDAT_ANONYMOUS_LOG\" '\n", + " 'not in '\n", + " 'os.environ:\\n'\n", + " ' '\n", + " 'os.environ[\"UVCDAT_ANONYMOUS_LOG\"] '\n", + " '= \"no\"\\n'\n", + " '\\n'\n", + " 'regions_specs '\n", + " '= {}\\n'\n", + " 'egg_pth = '\n", + " 'resources.resource_path()\\n'\n", + " 'exec(\\n'\n", + " ' '\n", + " 'compile(\\n'\n", + " ' '\n", + " 'open(os.path.join(egg_pth, '\n", + " '\"default_regions.py\")).read(),\\n'\n", + " ' '\n", + " 'os.path.join(egg_pth, '\n", + " '\"default_regions.py\"),\\n'\n", + " ' '\n", + " '\"exec\",\\n'\n", + " ' )\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Collect '\n", + " 'user '\n", + " 'defined '\n", + " 'options\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'P = '\n", + " 'pcmdi_metrics.driver.pmp_parser.PMPParser(\\n'\n", + " ' '\n", + " 'description=\"Runs '\n", + " 'PCMDI '\n", + " 'Modes of '\n", + " 'Variability '\n", + " 'Computations\",\\n'\n", + " ' '\n", + " 'formatter_class=RawTextHelpFormatter,\\n'\n", + " ')\\n'\n", + " 'P = '\n", + " 'AddParserArgument(P)\\n'\n", + " 'param = '\n", + " 'P.get_parameter()\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Pre-defined '\n", + " 'options\\n'\n", + " 'mip = '\n", + " 'param.mip\\n'\n", + " 'exp = '\n", + " 'param.exp\\n'\n", + " 'fq = '\n", + " 'param.frequency\\n'\n", + " 'realm = '\n", + " 'param.realm\\n'\n", + " 'print(\"mip:\", '\n", + " 'mip)\\n'\n", + " 'print(\"exp:\", '\n", + " 'exp)\\n'\n", + " 'print(\"fq:\", '\n", + " 'fq)\\n'\n", + " 'print(\"realm:\", '\n", + " 'realm)\\n'\n", + " '\\n'\n", + " '# On/off '\n", + " 'switches\\n'\n", + " 'obs_compare '\n", + " '= True # '\n", + " 'Statistics '\n", + " 'against '\n", + " 'observation\\n'\n", + " 'CBF = '\n", + " 'param.CBF '\n", + " '# Conduct '\n", + " 'CBF '\n", + " 'analysis\\n'\n", + " 'ConvEOF = '\n", + " 'param.ConvEOF '\n", + " '# Conduct '\n", + " 'conventional '\n", + " 'EOF '\n", + " 'analysis\\n'\n", + " '\\n'\n", + " 'EofScaling '\n", + " '= '\n", + " 'param.EofScaling '\n", + " '# If '\n", + " 'True, '\n", + " 'consider '\n", + " 'EOF with '\n", + " 'unit '\n", + " 'variance\\n'\n", + " 'RmDomainMean '\n", + " '= '\n", + " 'param.RemoveDomainMean '\n", + " '# If '\n", + " 'True, '\n", + " 'remove '\n", + " 'Domain '\n", + " 'Mean of '\n", + " 'each time '\n", + " 'step\\n'\n", + " 'LandMask '\n", + " '= '\n", + " 'param.landmask '\n", + " '# If '\n", + " 'True, '\n", + " 'maskout '\n", + " 'land '\n", + " 'region '\n", + " 'thus '\n", + " 'consider '\n", + " 'only over '\n", + " 'ocean\\n'\n", + " '\\n'\n", + " 'print(\"EofScaling:\", '\n", + " 'EofScaling)\\n'\n", + " 'print(\"RmDomainMean:\", '\n", + " 'RmDomainMean)\\n'\n", + " 'print(\"LandMask:\", '\n", + " 'LandMask)\\n'\n", + " '\\n'\n", + " 'nc_out_obs '\n", + " '= '\n", + " 'param.nc_out_obs '\n", + " '# Record '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " 'plot_obs '\n", + " '= '\n", + " 'param.plot_obs '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'nc_out_model '\n", + " '= '\n", + " 'param.nc_out '\n", + " '# Record '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " 'plot_model '\n", + " '= '\n", + " 'param.plot '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'update_json '\n", + " '= '\n", + " 'param.update_json\\n'\n", + " '\\n'\n", + " 'print(\"nc_out_obs, '\n", + " 'plot_obs:\", '\n", + " 'nc_out_obs, '\n", + " 'plot_obs)\\n'\n", + " 'print(\"nc_out_model, '\n", + " 'plot_model:\", '\n", + " 'nc_out_model, '\n", + " 'plot_model)\\n'\n", + " '\\n'\n", + " 'cmec = '\n", + " 'False\\n'\n", + " 'if '\n", + " 'hasattr(param, '\n", + " '\"cmec\"):\\n'\n", + " ' cmec '\n", + " '= '\n", + " 'param.cmec '\n", + " '# '\n", + " 'Generate '\n", + " 'CMEC '\n", + " 'compliant '\n", + " 'json\\n'\n", + " 'print(\"CMEC:\" '\n", + " '+ '\n", + " 'str(cmec))\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'given '\n", + " 'mode of '\n", + " 'variability\\n'\n", + " 'mode = '\n", + " 'VariabilityModeCheck(param.variability_mode, '\n", + " 'P)\\n'\n", + " 'print(\"mode:\", '\n", + " 'mode)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Variables\\n'\n", + " 'var = '\n", + " 'param.varModel\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'dependency '\n", + " 'for given '\n", + " 'season '\n", + " 'option\\n'\n", + " 'seasons = '\n", + " 'param.seasons\\n'\n", + " 'print(\"seasons:\", '\n", + " 'seasons)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Observation '\n", + " 'information\\n'\n", + " 'obs_name '\n", + " '= '\n", + " 'param.reference_data_name\\n'\n", + " 'obs_path '\n", + " '= '\n", + " 'param.reference_data_path\\n'\n", + " 'obs_var = '\n", + " 'param.varOBS\\n'\n", + " '\\n'\n", + " '# Path to '\n", + " 'model '\n", + " 'data as '\n", + " 'string '\n", + " 'template\\n'\n", + " 'modpath = '\n", + " 'StringConstructor(param.modpath)\\n'\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'modpath_lf '\n", + " '= '\n", + " 'StringConstructor(param.modpath_lf)\\n'\n", + " '\\n'\n", + " '# Check '\n", + " 'given '\n", + " 'model '\n", + " 'option\\n'\n", + " 'models = '\n", + " 'param.modnames\\n'\n", + " '\\n'\n", + " '# Include '\n", + " 'all '\n", + " 'models if '\n", + " 'conditioned\\n'\n", + " 'if (\"all\" '\n", + " 'in '\n", + " '[m.lower() '\n", + " 'for m in '\n", + " 'models]) '\n", + " 'or '\n", + " '(models '\n", + " '== '\n", + " '\"all\"):\\n'\n", + " ' '\n", + " 'model_index_path '\n", + " '= '\n", + " 'param.modpath.split(\"/\")[-1].split(\".\").index(\"%(model)\")\\n'\n", + " ' '\n", + " 'models = '\n", + " '[\\n'\n", + " ' '\n", + " 'p.split(\"/\")[-1].split(\".\")[model_index_path]\\n'\n", + " ' '\n", + " 'for p in '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=\"*\", '\n", + " 'realization=\"*\", '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' ]\\n'\n", + " ' # '\n", + " 'remove '\n", + " 'duplicates\\n'\n", + " ' '\n", + " 'models = '\n", + " 'sorted(list(dict.fromkeys(models)), '\n", + " 'key=lambda '\n", + " 's: '\n", + " 's.lower())\\n'\n", + " '\\n'\n", + " 'print(\"models:\", '\n", + " 'models)\\n'\n", + " 'print(\"number '\n", + " 'of '\n", + " 'models:\", '\n", + " 'len(models))\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Realizations\\n'\n", + " 'realization '\n", + " '= '\n", + " 'param.realization\\n'\n", + " 'print(\"realization: '\n", + " '\", '\n", + " 'realization)\\n'\n", + " '\\n'\n", + " '# EOF '\n", + " 'ordinal '\n", + " 'number\\n'\n", + " 'eofn_obs '\n", + " '= '\n", + " 'int(param.eofn_obs)\\n'\n", + " 'eofn_mod '\n", + " '= '\n", + " 'int(param.eofn_mod)\\n'\n", + " '\\n'\n", + " '# case '\n", + " 'id\\n'\n", + " 'case_id = '\n", + " 'param.case_id\\n'\n", + " '\\n'\n", + " '# Output\\n'\n", + " 'outdir_template '\n", + " '= '\n", + " 'param.process_templated_argument(\"results_dir\")\\n'\n", + " 'outdir = '\n", + " 'StringConstructor(\\n'\n", + " ' str(\\n'\n", + " ' '\n", + " 'outdir_template(\\n'\n", + " ' '\n", + " 'output_type=\"%(output_type)\",\\n'\n", + " ' '\n", + " 'mip=mip,\\n'\n", + " ' '\n", + " 'exp=exp,\\n'\n", + " ' '\n", + " 'variability_mode=mode,\\n'\n", + " ' '\n", + " 'reference_data_name=obs_name,\\n'\n", + " ' '\n", + " 'case_id=case_id,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' )\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# Debug\\n'\n", + " 'debug = '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# Year\\n'\n", + " 'msyear = '\n", + " 'param.msyear\\n'\n", + " 'meyear = '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear = '\n", + " 'param.osyear\\n'\n", + " 'oeyear = '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# lon1g '\n", + " 'and lon2g '\n", + " 'is for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' lon1g '\n", + " '= 0\\n'\n", + " ' lon2g '\n", + " '= 360\\n'\n", + " 'else:\\n'\n", + " ' lon1g '\n", + " '= -180\\n'\n", + " ' lon2g '\n", + " '= 180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, 0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, 31, '\n", + " '23, 59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, 0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, 31, '\n", + " '23, 59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for .json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' [\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' ]\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# Archive '\n", + " 'if there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if \"REF\" '\n", + " 'not in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear = '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs = '\n", + " '{}\\n'\n", + " ' '\n", + " 'pc_obs = '\n", + " '{}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for json '\n", + " 'archive\\n'\n", + " ' if '\n", + " '\"obs\" not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " '\"defaultReference\" '\n", + " 'not in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " '\"source\" '\n", + " 'not in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' if '\n", + " 'mode not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" + '\n", + " 'str(oeyear)\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - -\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' for '\n", + " 'season in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if season '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean (if '\n", + " 'needed),\\n'\n", + " ' # '\n", + " 'and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean of '\n", + " 'each time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' ) '\n", + " '= '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' ) '\n", + " '= '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - '\n", + " '-\\n'\n", + " ' # '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' # '\n", + " '. . . . . '\n", + " '. . . . . '\n", + " '. . . . . '\n", + " '. . . . . '\n", + " '. . . . '\n", + " '.\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Set '\n", + " 'output '\n", + " 'file name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" + '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] \" '\n", + " '+ '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] \" '\n", + " '+ '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'North '\n", + " 'test -- '\n", + " 'make this '\n", + " 'available '\n", + " 'as option '\n", + " 'later...\\n'\n", + " ' # '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- \", '\n", + " 'model, \" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' if '\n", + " 'model not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' )\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where run '\n", + " 'can be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' if '\n", + " 'realization '\n", + " '== \"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== \"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- \", '\n", + " 'run, \" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if run '\n", + " 'not in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if mode '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# read '\n", + " 'data in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear = '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: \" '\n", + " '+ '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " 'for '\n", + " 'season in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if season '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean (if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean of '\n", + " 'each time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " 'if CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if \"cbf\" '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# Save '\n", + " 'axes for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes = '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid to '\n", + " '0, so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) Give '\n", + " \"obs's \"\n", + " 'mask to '\n", + " 'model '\n", + " 'field, so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc = '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'cbf pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc / '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST ---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by cbf pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc / '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " '# Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " '# Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"cbf\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- plot '\n", + " 'map image '\n", + " 'to PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" + '\n", + " 'model + \" '\n", + " '(\" + run '\n", + " '+ \")\" + \" '\n", + " '- CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" + '\n", + " 'model + \" '\n", + " '(\" + run '\n", + " '+ \")\" + \" '\n", + " '- CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs = '\n", + " '\"eof\" + '\n", + " 'str(n + '\n", + " '1)\\n'\n", + " ' '\n", + " 'if eofs '\n", + " 'not in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac = '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv of '\n", + " 'pc time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc = '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Linear '\n", + " 'regression '\n", + " 'to have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " '# Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " '# Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr = '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr = '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF PC '\n", + " 'timeseries '\n", + " 'and usual '\n", + " 'model PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(n + '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# Save '\n", + " 'global '\n", + " 'map, pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- plot '\n", + " 'map image '\n", + " 'to PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" \"\\n'\n", + " ' '\n", + " '+ model\\n'\n", + " ' '\n", + " '+ \" (\"\\n'\n", + " ' '\n", + " '+ run\\n'\n", + " ' '\n", + " '+ \") - '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ str(n + '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode + '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" \"\\n'\n", + " ' '\n", + " '+ model\\n'\n", + " ' '\n", + " '+ \" (\"\\n'\n", + " ' '\n", + " '+ run\\n'\n", + " ' '\n", + " '+ \") - '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ str(n + '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'swap '\n", + " 'diagnosis\\n'\n", + " ' '\n", + " '# - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '- - - - - '\n", + " '-\\n'\n", + " ' '\n", + " 'rms_list.append(dict_head[\"rms\"])\\n'\n", + " ' '\n", + " 'cor_list.append(dict_head[\"cor\"])\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " ' '\n", + " 'tcor_list.append(dict_head[\"tcor_cbf_vs_eof_pc\"])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Find '\n", + " 'best '\n", + " 'matching '\n", + " 'eofs with '\n", + " 'different '\n", + " 'criteria\\n'\n", + " ' '\n", + " 'best_matching_eofs_rms '\n", + " '= '\n", + " 'rms_list.index(min(rms_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'best_matching_eofs_cor '\n", + " '= '\n", + " 'cor_list.index(max(cor_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " ' '\n", + " 'best_matching_eofs_tcor '\n", + " '= '\n", + " 'tcor_list.index(max(tcor_list)) '\n", + " '+ 1\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Save '\n", + " 'the best '\n", + " 'matching '\n", + " 'information '\n", + " 'to JSON\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season]\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__rms\"] '\n", + " '= '\n", + " 'best_matching_eofs_rms\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__cor\"] '\n", + " '= '\n", + " 'best_matching_eofs_cor\\n'\n", + " ' '\n", + " 'if CBF:\\n'\n", + " ' '\n", + " 'dict_head[\\n'\n", + " ' '\n", + " '\"best_matching_model_eofs__tcor_cbf_vs_eof_pc\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'best_matching_eofs_tcor\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'eof end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '=================================================================\\n'\n", + " ' '\n", + " '# '\n", + " 'Dictionary '\n", + " 'to JSON: '\n", + " 'individual '\n", + " 'JSON '\n", + " 'during '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " ' '\n", + " '# '\n", + " '-----------------------------------------------------------------\\n'\n", + " ' '\n", + " 'json_filename_tmp '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" + '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'variability_metrics_to_json(\\n'\n", + " ' '\n", + " 'outdir,\\n'\n", + " ' '\n", + " 'json_filename_tmp,\\n'\n", + " ' '\n", + " 'result_dict,\\n'\n", + " ' '\n", + " 'model=model,\\n'\n", + " ' '\n", + " 'run=run,\\n'\n", + " ' '\n", + " 'cmec_flag=cmec,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'except '\n", + " 'Exception '\n", + " 'as err:\\n'\n", + " ' '\n", + " 'if '\n", + " 'debug:\\n'\n", + " ' '\n", + " 'raise\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'print(\"warning: '\n", + " 'failed '\n", + " 'for \", '\n", + " 'model, '\n", + " 'run, '\n", + " 'err)\\n'\n", + " ' '\n", + " 'pass\\n'\n", + " '\\n'\n", + " '# '\n", + " '========================================================================\\n'\n", + " '# '\n", + " 'Dictionary '\n", + " 'to JSON: '\n", + " 'collective '\n", + " 'JSON at '\n", + " 'the end '\n", + " 'of '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " '# '\n", + " '------------------------------------------------------------------------\\n'\n", + " 'if not '\n", + " 'parallel '\n", + " 'and '\n", + " '(len(models) '\n", + " '> 1):\\n'\n", + " ' '\n", + " 'json_filename_all '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" + '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " '\"allModels\",\\n'\n", + " ' '\n", + " '\"allRuns\",\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ 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" 'model_index_path '\n", + " '= '\n", + " 'param.modpath.split(\"/\")[-1].split(\".\").index(\"%(model)\")\\n'\n", + " ' '\n", + " 'models '\n", + " '= [\\n'\n", + " ' '\n", + " 'p.split(\"/\")[-1].split(\".\")[model_index_path]\\n'\n", + " ' '\n", + " 'for p '\n", + " 'in '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=\"*\", '\n", + " 'realization=\"*\", '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' # '\n", + " 'remove '\n", + " 'duplicates\\n'\n", + " ' '\n", + " 'models '\n", + " '= '\n", + " 'sorted(list(dict.fromkeys(models)), '\n", + " 'key=lambda '\n", + " 's: '\n", + " 's.lower())\\n'\n", + " '\\n'\n", + " 'print(\"models:\", '\n", + " 'models)\\n'\n", + " 'print(\"number '\n", + " 'of '\n", + " 'models:\", '\n", + " 'len(models))\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Realizations\\n'\n", + " 'realization '\n", + " '= '\n", + " 'param.realization\\n'\n", + " 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" ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Debug\\n'\n", + " 'debug '\n", + " '= '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Year\\n'\n", + " 'msyear '\n", + " '= '\n", + " 'param.msyear\\n'\n", + " 'meyear '\n", + " '= '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear '\n", + " '= '\n", + " 'param.osyear\\n'\n", + " 'oeyear '\n", + " '= '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# '\n", + " 'lon1g '\n", + " 'and '\n", + " 'lon2g '\n", + " 'is '\n", + " 'for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if '\n", + " 'mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= 0\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '360\\n'\n", + " 'else:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= '\n", + " '-180\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables 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'=================================================\\n'\n", + " '# '\n", + " 'Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' '\n", + " 'if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if '\n", + " 'there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' '\n", + " 'if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if '\n", + " '\"REF\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to '\n", + " 'keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for '\n", + " 'json '\n", + " 'archive\\n'\n", + " ' '\n", + " 'if '\n", + " '\"obs\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"source\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '-\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'North '\n", + " 'test '\n", + " '-- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- '\n", + " '\", '\n", + " 'model, '\n", + " '\" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'model '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run '\n", + " 'can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " 'for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- '\n", + " '\", '\n", + " 'run, '\n", + " '\" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'run '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: '\n", + " '\" + '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = '\n", + " '{}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " '\"cbf\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid '\n", + " 'to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to '\n", + " 'that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'axes '\n", + " 'for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes '\n", + " '= '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid '\n", + " 'to 0, '\n", + " 'so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect '\n", + " 'to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) '\n", + " 'Give '\n", + " \"obs's \"\n", + " 'mask '\n", + " 'to '\n", + " 'model '\n", + " 'field, '\n", + " 'so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF '\n", + " 'PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '= '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of '\n", + " 'cbf '\n", + " 'pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# '\n", + " 'cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# '\n", + " 'native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' 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\"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n '\n", + " 'in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs '\n", + " '= '\n", + " '\"eof\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ 1)\\n'\n", + " ' '\n", + " 'if '\n", + " 'eofs '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for '\n", + " 'each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac '\n", + " '= '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF '\n", + " 'PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model '\n", + " 'PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'swap '\n", + " 'diagnosis\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list.append(dict_head[\"rms\"])\\n'\n", + " ' '\n", + " 'cor_list.append(dict_head[\"cor\"])\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tcor_list.append(dict_head[\"tcor_cbf_vs_eof_pc\"])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Find '\n", + " 'best '\n", + " 'matching '\n", + " 'eofs '\n", + " 'with '\n", + " 'different '\n", + " 'criteria\\n'\n", + " ' '\n", + " 'best_matching_eofs_rms '\n", + " '= '\n", + " 'rms_list.index(min(rms_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'best_matching_eofs_cor '\n", + " '= '\n", + " 'cor_list.index(max(cor_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'best_matching_eofs_tcor '\n", + " '= '\n", + " 'tcor_list.index(max(tcor_list)) '\n", + " '+ 1\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'the '\n", + " 'best '\n", + " 'matching '\n", + " 'information '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season]\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__rms\"] '\n", + " '= '\n", + " 'best_matching_eofs_rms\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__cor\"] '\n", + " '= '\n", + " 'best_matching_eofs_cor\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'dict_head[\\n'\n", + " ' '\n", + " '\"best_matching_model_eofs__tcor_cbf_vs_eof_pc\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'best_matching_eofs_tcor\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'eof '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '=================================================================\\n'\n", + " ' '\n", + " '# '\n", + " 'Dictionary '\n", + " 'to '\n", + " 'JSON: '\n", + " 'individual '\n", + " 'JSON '\n", + " 'during '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " ' '\n", + " '# '\n", + " '-----------------------------------------------------------------\\n'\n", + " ' '\n", + " 'json_filename_tmp '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'variability_metrics_to_json(\\n'\n", + " ' '\n", + " 'outdir,\\n'\n", + " ' '\n", + " 'json_filename_tmp,\\n'\n", + " ' '\n", + " 'result_dict,\\n'\n", + " ' '\n", + " 'model=model,\\n'\n", + " ' '\n", + " 'run=run,\\n'\n", + " ' '\n", + " 'cmec_flag=cmec,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'except '\n", + " 'Exception '\n", + " 'as '\n", + " 'err:\\n'\n", + " ' '\n", + " 'if '\n", + " 'debug:\\n'\n", + " ' '\n", + " 'raise\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'print(\"warning: '\n", + " 'failed '\n", + " 'for '\n", + " '\", '\n", + " 'model, '\n", + " 'run, '\n", + " 'err)\\n'\n", + " ' '\n", + " 'pass\\n'\n", + " '\\n'\n", + " '# 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'\n", + " 'Neither '\n", + " 'the '\n", + " 'United '\n", + " 'States '\n", + " 'government\\n'\n", + " 'nor '\n", + " 'Lawrence '\n", + " 'Livermore '\n", + " 'National '\n", + " 'Security, '\n", + " 'LLC, '\n", + " 'nor '\n", + " 'any '\n", + " 'of '\n", + " 'their '\n", + " 'employees\\n'\n", + " 'makes '\n", + " 'any '\n", + " 'warranty, '\n", + " 'expressed '\n", + " 'or '\n", + " 'implied, '\n", + " 'or '\n", + " 'assumes '\n", + " 'any '\n", + " 'legal '\n", + " 'liability '\n", + " 'or\\n'\n", + " 'responsibility '\n", + " 'for '\n", + " 'the '\n", + " 'accuracy, '\n", + " 'completeness, '\n", + " 'or '\n", + " 'usefulness '\n", + " 'of '\n", + " 'any\\n'\n", + " 'information, '\n", + " 'apparatus, '\n", + " 'product, '\n", + " 'or '\n", + " 'process '\n", + " 'disclosed, '\n", + " 'or '\n", + " 'represents '\n", + " 'that '\n", + " 'its\\n'\n", + " 'use '\n", + " 'would '\n", + " 'not '\n", + " 'infringe '\n", + " 'privately '\n", + " 'owned '\n", + " 'rights. 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'pcmdi_metrics.driver.pmp_parser.PMPParser(\\n'\n", + " ' '\n", + " 'description=\"Runs '\n", + " 'PCMDI '\n", + " 'Modes '\n", + " 'of '\n", + " 'Variability '\n", + " 'Computations\",\\n'\n", + " ' '\n", + " 'formatter_class=RawTextHelpFormatter,\\n'\n", + " ')\\n'\n", + " 'P = '\n", + " 'AddParserArgument(P)\\n'\n", + " 'param '\n", + " '= '\n", + " 'P.get_parameter()\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Pre-defined '\n", + " 'options\\n'\n", + " 'mip = '\n", + " 'param.mip\\n'\n", + " 'exp = '\n", + " 'param.exp\\n'\n", + " 'fq = '\n", + " 'param.frequency\\n'\n", + " 'realm '\n", + " '= '\n", + " 'param.realm\\n'\n", + " 'print(\"mip:\", '\n", + " 'mip)\\n'\n", + " 'print(\"exp:\", '\n", + " 'exp)\\n'\n", + " 'print(\"fq:\", '\n", + " 'fq)\\n'\n", + " 'print(\"realm:\", '\n", + " 'realm)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'On/off '\n", + " 'switches\\n'\n", + " 'obs_compare '\n", + " '= '\n", + " 'True '\n", + " '# '\n", + " 'Statistics '\n", + " 'against '\n", + " 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'EofScaling)\\n'\n", + " 'print(\"RmDomainMean:\", '\n", + " 'RmDomainMean)\\n'\n", + " 'print(\"LandMask:\", '\n", + " 'LandMask)\\n'\n", + " '\\n'\n", + " 'nc_out_obs '\n", + " '= '\n", + " 'param.nc_out_obs '\n", + " '# '\n", + " 'Record '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " 'plot_obs '\n", + " '= '\n", + " 'param.plot_obs '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'nc_out_model '\n", + " '= '\n", + " 'param.nc_out '\n", + " '# '\n", + " 'Record '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " 'plot_model '\n", + " '= '\n", + " 'param.plot '\n", + " '# '\n", + " 'Generate '\n", + " 'plots\\n'\n", + " 'update_json '\n", + " '= '\n", + " 'param.update_json\\n'\n", + " '\\n'\n", + " 'print(\"nc_out_obs, '\n", + " 'plot_obs:\", '\n", + " 'nc_out_obs, '\n", + " 'plot_obs)\\n'\n", + " 'print(\"nc_out_model, '\n", + " 'plot_model:\", '\n", + " 'nc_out_model, '\n", + " 'plot_model)\\n'\n", + " '\\n'\n", + " 'cmec '\n", + " '= '\n", + " 'False\\n'\n", + " 'if 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'param.reference_data_name\\n'\n", + " 'obs_path '\n", + " '= '\n", + " 'param.reference_data_path\\n'\n", + " 'obs_var '\n", + " '= '\n", + " 'param.varOBS\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Path '\n", + " 'to '\n", + " 'model '\n", + " 'data '\n", + " 'as '\n", + " 'string '\n", + " 'template\\n'\n", + " 'modpath '\n", + " '= '\n", + " 'StringConstructor(param.modpath)\\n'\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'modpath_lf '\n", + " '= '\n", + " 'StringConstructor(param.modpath_lf)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Check '\n", + " 'given '\n", + " 'model '\n", + " 'option\\n'\n", + " 'models '\n", + " '= '\n", + " 'param.modnames\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Include '\n", + " 'all '\n", + " 'models '\n", + " 'if '\n", + " 'conditioned\\n'\n", + " 'if '\n", + " '(\"all\" '\n", + " 'in '\n", + " '[m.lower() '\n", + " 'for m '\n", + " 'in '\n", + " 'models]) '\n", + " 'or '\n", + " '(models '\n", + " '== '\n", + " '\"all\"):\\n'\n", + " ' '\n", + " 'model_index_path '\n", + " '= '\n", + " 'param.modpath.split(\"/\")[-1].split(\".\").index(\"%(model)\")\\n'\n", + " ' '\n", + " 'models '\n", + " '= [\\n'\n", + " ' '\n", + " 'p.split(\"/\")[-1].split(\".\")[model_index_path]\\n'\n", + " ' '\n", + " 'for p '\n", + " 'in '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=\"*\", '\n", + " 'realization=\"*\", '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' # '\n", + " 'remove '\n", + " 'duplicates\\n'\n", + " ' '\n", + " 'models '\n", + " '= '\n", + " 'sorted(list(dict.fromkeys(models)), '\n", + " 'key=lambda '\n", + " 's: '\n", + " 's.lower())\\n'\n", + " '\\n'\n", + " 'print(\"models:\", '\n", + " 'models)\\n'\n", + " 'print(\"number '\n", + " 'of '\n", + " 'models:\", '\n", + " 'len(models))\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Realizations\\n'\n", + " 'realization '\n", + " '= '\n", + " 'param.realization\\n'\n", + " 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" ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Debug\\n'\n", + " 'debug '\n", + " '= '\n", + " 'param.debug\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Year\\n'\n", + " 'msyear '\n", + " '= '\n", + " 'param.msyear\\n'\n", + " 'meyear '\n", + " '= '\n", + " 'param.meyear\\n'\n", + " 'YearCheck(msyear, '\n", + " 'meyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " 'osyear '\n", + " '= '\n", + " 'param.osyear\\n'\n", + " 'oeyear '\n", + " '= '\n", + " 'param.oeyear\\n'\n", + " 'YearCheck(osyear, '\n", + " 'oeyear, '\n", + " 'P)\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Units '\n", + " 'adjustment\\n'\n", + " 'ObsUnitsAdjust '\n", + " '= '\n", + " 'param.ObsUnitsAdjust\\n'\n", + " 'ModUnitsAdjust '\n", + " '= '\n", + " 'param.ModUnitsAdjust\\n'\n", + " '\\n'\n", + " '# '\n", + " 'lon1g '\n", + " 'and '\n", + " 'lon2g '\n", + " 'is '\n", + " 'for '\n", + " 'global '\n", + " 'map '\n", + " 'plotting\\n'\n", + " 'if '\n", + " 'mode '\n", + " 'in '\n", + " '[\"PDO\", '\n", + " '\"NPGO\"]:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= 0\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '360\\n'\n", + " 'else:\\n'\n", + " ' '\n", + " 'lon1g '\n", + " '= '\n", + " '-180\\n'\n", + " ' '\n", + " 'lon2g '\n", + " '= '\n", + " '180\\n'\n", + " '\\n'\n", + " '# '\n", + " 'parallel\\n'\n", + " 'parallel '\n", + " '= '\n", + " 'param.parallel\\n'\n", + " 'print(\"parallel:\", '\n", + " 'parallel)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Time '\n", + " 'period '\n", + " 'adjustment\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'start_time '\n", + " '= '\n", + " 'cdtime.comptime(msyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " 'end_time '\n", + " '= '\n", + " 'cdtime.comptime(meyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " '\\n'\n", + " 'try:\\n'\n", + " ' # '\n", + " 'osyear '\n", + " 'and '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'defined.\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(osyear, '\n", + " '1, 1, '\n", + " '0, '\n", + " '0)\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'cdtime.comptime(oeyear, '\n", + " '12, '\n", + " '31, '\n", + " '23, '\n", + " '59)\\n'\n", + " 'except '\n", + " 'NameError:\\n'\n", + " ' # '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " 'variables '\n", + " 'were '\n", + " 'NOT '\n", + " 'defined\\n'\n", + " ' '\n", + " 'start_time_obs '\n", + " '= '\n", + " 'start_time\\n'\n", + " ' '\n", + " 'end_time_obs '\n", + " '= '\n", + " 'end_time\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Region '\n", + " 'control\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'region_subdomain '\n", + " '= '\n", + " 'get_domain_range(mode, '\n", + " 'regions_specs)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Create '\n", + " 'output '\n", + " 'directories\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'output_type '\n", + " 'in '\n", + " '[\"graphics\", '\n", + " '\"diagnostic_results\", '\n", + " '\"metrics_results\"]:\\n'\n", + " ' '\n", + " 'if '\n", + " 'not '\n", + " 'os.path.exists(outdir(output_type=output_type)):\\n'\n", + " ' '\n", + " 'os.makedirs(outdir(output_type=output_type))\\n'\n", + " ' '\n", + " 'print(outdir(output_type=output_type))\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# Set '\n", + " 'dictionary '\n", + " 'for '\n", + " '.json '\n", + " 'record\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'result_dict '\n", + " '= '\n", + " 'tree()\\n'\n", + " '\\n'\n", + " '# Set '\n", + " 'metrics '\n", + " 'output '\n", + " 'JSON '\n", + " 'file\\n'\n", + " 'json_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " 'json_file '\n", + " '= '\n", + " 'os.path.join(outdir(output_type=\"metrics_results\"), '\n", + " 'json_filename '\n", + " '+ '\n", + " '\".json\")\\n'\n", + " 'json_file_org '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"metrics_results\"),\\n'\n", + " ' '\n", + " '\"_\".join([json_filename, '\n", + " '\"org\", '\n", + " 'str(os.getpid())]) '\n", + " '+ '\n", + " '\".json\",\\n'\n", + " ')\\n'\n", + " '\\n'\n", + " '# '\n", + " 'Archive '\n", + " 'if '\n", + " 'there '\n", + " 'is '\n", + " 'pre-existing '\n", + " 'JSON: '\n", + " 'preventing '\n", + " 'overwriting\\n'\n", + " 'if '\n", + " 'os.path.isfile(json_file) '\n", + " 'and '\n", + " 'os.stat(json_file).st_size '\n", + " '> 0:\\n'\n", + " ' '\n", + " 'copyfile(json_file, '\n", + " 'json_file_org)\\n'\n", + " ' '\n", + " 'if '\n", + " 'update_json:\\n'\n", + " ' '\n", + " 'fj = '\n", + " 'open(json_file)\\n'\n", + " ' '\n", + " 'result_dict '\n", + " '= '\n", + " 'json.loads(fj.read())\\n'\n", + " ' '\n", + " 'fj.close()\\n'\n", + " '\\n'\n", + " 'if '\n", + " '\"REF\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"] '\n", + " '= {}\\n'\n", + " 'if '\n", + " '\"RESULTS\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict.keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Observation\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'obs_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'osyear, '\n", + " 'oeyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'obs_path,\\n'\n", + " ' '\n", + " 'obs_lf_path,\\n'\n", + " ' '\n", + " 'obs_var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time_obs,\\n'\n", + " ' '\n", + " 'end_time_obs,\\n'\n", + " ' '\n", + " 'ObsUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Save '\n", + " 'global '\n", + " 'grid '\n", + " 'information '\n", + " 'for '\n", + " 'regrid '\n", + " 'below\\n'\n", + " ' '\n", + " 'ref_grid_global '\n", + " '= '\n", + " 'obs_timeseries.getGrid()\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Declare '\n", + " 'dictionary '\n", + " 'variables '\n", + " 'to '\n", + " 'keep '\n", + " 'information '\n", + " 'from '\n", + " 'observation\\n'\n", + " ' '\n", + " 'eof_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'pc_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'frac_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'solver_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'reverse_sign_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'eof_lr_obs '\n", + " '= {}\\n'\n", + " ' '\n", + " 'stdv_pc_obs '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Dictonary '\n", + " 'for '\n", + " 'json '\n", + " 'archive\\n'\n", + " ' '\n", + " 'if '\n", + " '\"obs\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"source\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"REF\"][\"obs\"][\"defaultReference\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"source\"] '\n", + " '= '\n", + " 'obs_path\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"reference_eofs\"] '\n", + " '= '\n", + " 'eofn_obs\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][\"period\"] '\n", + " '= (\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' # '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '-\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'season '\n", + " 'loop '\n", + " 'starts\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'dict_head_obs '\n", + " '= '\n", + " 'result_dict[\"REF\"][\"obs\"][\"defaultReference\"][mode][season]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'obs_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'obs_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain '\n", + " '= '\n", + " 'obs_timeseries_season(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"EOF '\n", + " 'analysis\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'obs_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_obs,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'debug_print(\"calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season] '\n", + " '= '\n", + " 'calcSTD(pc_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. . . '\n", + " '. .\\n'\n", + " ' '\n", + " 'debug_print(\"record '\n", + " 'results\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_obs),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " '\"obs\",\\n'\n", + " ' '\n", + " 'str(osyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(oeyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename_obs '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_obs:\\n'\n", + " ' '\n", + " 'output_nc_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file_obs,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'pc_obs[season],\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'slope_obs,\\n'\n", + " ' '\n", + " 'intercept_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Plotting\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_obs:\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename_obs\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode, '\n", + " \"'[REF] \"\n", + " \"'+obs_name, \"\n", + " 'osyear, '\n", + " 'oeyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof_obs[season], '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file_obs+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](region_subdomain),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " '\"[REF] '\n", + " '\" + '\n", + " 'obs_name,\\n'\n", + " ' '\n", + " 'osyear,\\n'\n", + " ' '\n", + " 'oeyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_obs[season](longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_obs[season],\\n'\n", + " ' '\n", + " 'output_img_file_obs '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'plotting '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'stdv '\n", + " 'of PC '\n", + " 'time '\n", + " 'series '\n", + " 'in '\n", + " 'dictionary\\n'\n", + " ' '\n", + " 'dict_head_obs[\"stdv_pc\"] '\n", + " '= '\n", + " 'stdv_pc_obs[season]\\n'\n", + " ' '\n", + " 'dict_head_obs[\"frac\"] '\n", + " '= '\n", + " 'float(frac_obs[season])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Mean\\n'\n", + " ' '\n", + " 'mean_obs '\n", + " '= '\n", + " 'cdutil.averager(eof_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\")\\n'\n", + " ' '\n", + " 'mean_glo_obs '\n", + " '= '\n", + " 'cdutil.averager(\\n'\n", + " ' '\n", + " 'eof_lr_obs[season], '\n", + " 'axis=\"yx\", '\n", + " 'weights=\"weighted\"\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean\"] '\n", + " '= '\n", + " 'float(mean_obs)\\n'\n", + " ' '\n", + " 'dict_head_obs[\"mean_glo\"] '\n", + " '= '\n", + " 'float(mean_glo_obs)\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'mean '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'North '\n", + " 'test '\n", + " '-- '\n", + " 'make '\n", + " 'this '\n", + " 'available '\n", + " 'as '\n", + " 'option '\n", + " 'later...\\n'\n", + " ' '\n", + " '# '\n", + " \"execfile('../north_test.py')\\n\"\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"obs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " '# '\n", + " '=================================================\\n'\n", + " '# '\n", + " 'Model\\n'\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " 'for '\n", + " 'model '\n", + " 'in '\n", + " 'models:\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '----- '\n", + " '\", '\n", + " 'model, '\n", + " '\" '\n", + " '---------------------\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'model '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model] '\n", + " '= {}\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'glob.glob(\\n'\n", + " ' '\n", + " 'modpath(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'model_path_list '\n", + " '= '\n", + " 'sort_human(model_path_list)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"model_path_list: '\n", + " '\" + '\n", + " 'str(model_path_list), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' # '\n", + " 'Find '\n", + " 'where '\n", + " 'run '\n", + " 'can '\n", + " 'be '\n", + " 'gripped '\n", + " 'from '\n", + " 'given '\n", + " 'filename '\n", + " 'template '\n", + " 'for '\n", + " 'modpath\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run_in_modpath '\n", + " '= (\\n'\n", + " ' '\n", + " 'modpath(\\n'\n", + " ' '\n", + " 'mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model, '\n", + " 'realization=realization, '\n", + " 'variable=var\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '.split(\"/\")[-1]\\n'\n", + " ' '\n", + " '.split(\".\")\\n'\n", + " ' '\n", + " '.index(realization)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' # '\n", + " 'Run\\n'\n", + " ' # '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " 'for '\n", + " 'model_path '\n", + " 'in '\n", + " 'model_path_list:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'try:\\n'\n", + " ' '\n", + " 'if '\n", + " 'realization '\n", + " '== '\n", + " '\"*\":\\n'\n", + " ' '\n", + " 'run = '\n", + " '(model_path.split(\"/\")[-1]).split(\".\")[run_in_modpath]\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'run = '\n", + " 'realization\\n'\n", + " ' '\n", + " 'print(\" '\n", + " '--- '\n", + " '\", '\n", + " 'run, '\n", + " '\" '\n", + " '---\")\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'run '\n", + " 'not '\n", + " 'in '\n", + " 'list(result_dict[\"RESULTS\"][model].keys()):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " '\"defaultReference\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'if '\n", + " 'mode '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " '\"target_model_eofs\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'eofn_mod\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'LandMask:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'modpath_lf(mip=mip, '\n", + " 'exp=exp, '\n", + " 'model=model)\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'model_lf_path '\n", + " '= '\n", + " 'None\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'read '\n", + " 'data '\n", + " 'in\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'msyear, '\n", + " 'meyear '\n", + " '= '\n", + " 'read_data_in(\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'model_path,\\n'\n", + " ' '\n", + " 'model_lf_path,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " 'start_time,\\n'\n", + " ' '\n", + " 'end_time,\\n'\n", + " ' '\n", + " 'ModUnitsAdjust,\\n'\n", + " ' '\n", + " 'LandMask,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"msyear: '\n", + " '\" + '\n", + " 'str(msyear) '\n", + " '+ \" '\n", + " 'meyear: '\n", + " '\" + '\n", + " 'str(meyear), '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Season '\n", + " 'loop\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'for '\n", + " 'season '\n", + " 'in '\n", + " 'seasons:\\n'\n", + " ' '\n", + " 'debug_print(\"season: '\n", + " '\" + '\n", + " 'season, '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " 'season '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '] = '\n", + " '{}\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][season][\\n'\n", + " ' '\n", + " '\"period\"\\n'\n", + " ' '\n", + " '] = '\n", + " '(str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear))\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Time '\n", + " 'series '\n", + " 'adjustment '\n", + " '(remove '\n", + " 'annual '\n", + " 'cycle, '\n", + " 'seasonal '\n", + " 'mean '\n", + " '(if '\n", + " 'needed),\\n'\n", + " ' '\n", + " '# and '\n", + " 'subtracting '\n", + " 'domain '\n", + " '(or '\n", + " 'global) '\n", + " 'mean '\n", + " 'of '\n", + " 'each '\n", + " 'time '\n", + " 'step)\\n'\n", + " ' '\n", + " 'debug_print(\"time '\n", + " 'series '\n", + " 'adjustment\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season '\n", + " '= '\n", + " 'adjust_timeseries(\\n'\n", + " ' '\n", + " 'model_timeseries, '\n", + " 'mode, '\n", + " 'season, '\n", + " 'region_subdomain, '\n", + " 'RmDomainMean\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain\\n'\n", + " ' '\n", + " 'debug_print(\"extract '\n", + " 'subdomain\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain '\n", + " '= '\n", + " 'model_timeseries_season(\\n'\n", + " ' '\n", + " 'region_subdomain\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Common '\n", + " 'Basis '\n", + " 'Function '\n", + " 'Approach\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF '\n", + " 'and '\n", + " 'obs_compare:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'if '\n", + " '\"cbf\" '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][mode][\\n'\n", + " ' '\n", + " 'season\\n'\n", + " ' '\n", + " '][\"cbf\"] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][\"cbf\"]\\n'\n", + " ' '\n", + " 'debug_print(\"CBF '\n", + " 'approach '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Regrid '\n", + " '(interpolation, '\n", + " 'model '\n", + " 'grid '\n", + " 'to '\n", + " 'ref '\n", + " 'grid)\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid '\n", + " '= '\n", + " 'model_timeseries_season.regrid(\\n'\n", + " ' '\n", + " 'ref_grid_global, '\n", + " 'regridTool=\"regrid2\", '\n", + " 'mkCyclic=True\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= (\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid(region_subdomain)\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Matching '\n", + " \"model's \"\n", + " 'missing '\n", + " 'value '\n", + " 'location '\n", + " 'to '\n", + " 'that '\n", + " 'of '\n", + " 'observation\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'axes '\n", + " 'for '\n", + " 'preserving\\n'\n", + " ' '\n", + " 'axes '\n", + " '= '\n", + " 'model_timeseries_season_regrid_subdomain.getAxisList()\\n'\n", + " ' '\n", + " '# 1) '\n", + " 'Replace '\n", + " \"model's \"\n", + " 'masked '\n", + " 'grid '\n", + " 'to 0, '\n", + " 'so '\n", + " 'theoritically '\n", + " \"won't \"\n", + " 'affect '\n", + " 'to '\n", + " 'result\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain '\n", + " '= '\n", + " 'MV2.array(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.filled(0.0)\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " '# 2) '\n", + " 'Give '\n", + " \"obs's \"\n", + " 'mask '\n", + " 'to '\n", + " 'model '\n", + " 'field, '\n", + " 'so '\n", + " 'enable '\n", + " 'projecField '\n", + " 'functionality '\n", + " 'below\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.mask '\n", + " '= '\n", + " 'eof_obs[season].mask\\n'\n", + " ' '\n", + " '# '\n", + " 'Preserve '\n", + " 'axes\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain.setAxisList(axes)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# CBF '\n", + " 'PC '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '= '\n", + " 'gain_pseudo_pcs(\\n'\n", + " ' '\n", + " 'solver_obs[season],\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eofn_obs,\\n'\n", + " ' '\n", + " 'reverse_sign_obs[season],\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of '\n", + " 'cbf '\n", + " 'pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_cbf_pc '\n", + " '= '\n", + " 'calcSTD(cbf_pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map; '\n", + " 'teleconnection '\n", + " 'purpose\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " '# '\n", + " 'cbf_pc, '\n", + " 'model_timeseries_season_regrid, '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Extract '\n", + " 'subdomain '\n", + " 'for '\n", + " 'statistics\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain '\n", + " '= '\n", + " 'eof_lr_cbf(region_subdomain)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc\\n'\n", + " ' '\n", + " 'frac_cbf '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " '# '\n", + " 'model_timeseries_season_regrid_subdomain, '\n", + " '# '\n", + " 'regridded '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain, '\n", + " '# '\n", + " 'native '\n", + " 'grid '\n", + " 'model '\n", + " 'anomaly '\n", + " 'space\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'SENSITIVITY '\n", + " 'TEST '\n", + " '---\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'fraction '\n", + " 'of '\n", + " 'variance '\n", + " 'explained '\n", + " 'by '\n", + " 'cbf '\n", + " 'pc '\n", + " '(on '\n", + " 'regrid '\n", + " 'domain)\\n'\n", + " ' '\n", + " 'frac_cbf_regrid '\n", + " '= '\n", + " 'gain_pcs_fraction(\\n'\n", + " ' '\n", + " 'model_timeseries_season_regrid_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'cbf_pc '\n", + " '/ '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'dict_head[\"frac_cbf_regrid\"] '\n", + " '= '\n", + " 'float(frac_cbf_regrid)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr_cbf '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof_lr_cbf_subdomain,\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'stdv_cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"cbf\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " 'eof_lr_cbf,\\n'\n", + " ' '\n", + " 'cbf_pc,\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'slope_cbf,\\n'\n", + " ' '\n", + " 'intercept_cbf,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(region_subdomain),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper() '\n", + " '+ \" \" '\n", + " '+ '\n", + " 'model '\n", + " '+ \" '\n", + " '(\" + '\n", + " 'run + '\n", + " '\")\" + '\n", + " '\" - '\n", + " 'CBF\",\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr_cbf(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac_cbf,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_cbf_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'pcs '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# '\n", + " 'Conventional '\n", + " 'EOF '\n", + " 'approach '\n", + " 'as '\n", + " 'supplementary\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'if '\n", + " 'ConvEOF:\\n'\n", + " '\\n'\n", + " ' '\n", + " 'eofn_mod_max '\n", + " '= 3\\n'\n", + " '\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'analysis\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'start\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_list,\\n'\n", + " ' '\n", + " 'pc_list,\\n'\n", + " ' '\n", + " 'frac_list,\\n'\n", + " ' '\n", + " 'reverse_sign_list,\\n'\n", + " ' '\n", + " 'solver,\\n'\n", + " ' '\n", + " ') = '\n", + " 'eof_analysis_get_variance_mode(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'model_timeseries_season_subdomain,\\n'\n", + " ' '\n", + " 'eofn=eofn_mod,\\n'\n", + " ' '\n", + " 'eofn_max=eofn_mod_max,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " 'EofScaling=EofScaling,\\n'\n", + " ' '\n", + " 'save_multiple_eofs=True,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'EOF '\n", + " 'analysis '\n", + " 'done\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '-------------------------------------------------\\n'\n", + " ' '\n", + " '# For '\n", + " 'multiple '\n", + " 'EOFs '\n", + " '(e.g., '\n", + " 'EOF1, '\n", + " 'EOF2, '\n", + " 'EOF3, '\n", + " '...)\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'cor_list '\n", + " '= []\\n'\n", + " ' '\n", + " 'tcor_list '\n", + " '= []\\n'\n", + " '\\n'\n", + " ' '\n", + " 'for n '\n", + " 'in '\n", + " 'range(0, '\n", + " 'eofn_mod_max):\\n'\n", + " ' '\n", + " 'eofs '\n", + " '= '\n", + " '\"eof\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ 1)\\n'\n", + " ' '\n", + " 'if '\n", + " 'eofs '\n", + " 'not '\n", + " 'in '\n", + " 'list(\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season].keys()\\n'\n", + " ' '\n", + " '):\\n'\n", + " ' '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season][eofs] '\n", + " '= {}\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\\n'\n", + " ' '\n", + " '\"defaultReference\"\\n'\n", + " ' '\n", + " '][mode][season][eofs]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Component '\n", + " 'for '\n", + " 'each '\n", + " 'EOFs\\n'\n", + " ' '\n", + " 'eof = '\n", + " 'eof_list[n]\\n'\n", + " ' '\n", + " 'pc = '\n", + " 'pc_list[n]\\n'\n", + " ' '\n", + " 'frac '\n", + " '= '\n", + " 'frac_list[n]\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Calculate '\n", + " 'stdv '\n", + " 'of pc '\n", + " 'time '\n", + " 'series\\n'\n", + " ' '\n", + " 'stdv_pc '\n", + " '= '\n", + " 'calcSTD(pc)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Linear '\n", + " 'regression '\n", + " 'to '\n", + " 'have '\n", + " 'extended '\n", + " 'global '\n", + " 'map:\\n'\n", + " ' '\n", + " '(\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'intercept,\\n'\n", + " ' '\n", + " ') = '\n", + " 'linear_regression_on_globe_for_teleconnection(\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'model_timeseries_season,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'RmDomainMean,\\n'\n", + " ' '\n", + " 'EofScaling,\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Record '\n", + " 'results\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# '\n", + " 'Metrics '\n", + " 'results '\n", + " '-- '\n", + " 'statistics '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'if '\n", + " 'obs_compare:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'eof_obs=eof_obs[season],\\n'\n", + " ' '\n", + " 'eof_lr_obs=eof_lr_obs[season],\\n'\n", + " ' '\n", + " 'stdv_pc_obs=stdv_pc_obs[season],\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'dict_head, '\n", + " 'eof_lr '\n", + " '= '\n", + " 'calc_stats_save_dict(\\n'\n", + " ' '\n", + " 'dict_head,\\n'\n", + " ' '\n", + " 'eof,\\n'\n", + " ' '\n", + " 'eof_lr,\\n'\n", + " ' '\n", + " 'slope,\\n'\n", + " ' '\n", + " 'pc,\\n'\n", + " ' '\n", + " 'stdv_pc,\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'region_subdomain,\\n'\n", + " ' '\n", + " 'obs_compare=obs_compare,\\n'\n", + " ' '\n", + " 'method=\"eof\",\\n'\n", + " ' '\n", + " 'debug=debug,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Temporal '\n", + " 'correlation '\n", + " 'between '\n", + " 'CBF '\n", + " 'PC '\n", + " 'timeseries '\n", + " 'and '\n", + " 'usual '\n", + " 'model '\n", + " 'PC '\n", + " 'timeseries\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tc = '\n", + " 'calcTCOR(cbf_pc, '\n", + " 'pc)\\n'\n", + " ' '\n", + " 'debug_print(\"cbf '\n", + " 'tc '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " ' '\n", + " 'dict_head[\"tcor_cbf_vs_eof_pc\"] '\n", + " '= tc\\n'\n", + " '\\n'\n", + " ' '\n", + " '# Set '\n", + " 'output '\n", + " 'file '\n", + " 'name '\n", + " 'for '\n", + " 'NetCDF '\n", + " 'and '\n", + " 'plot '\n", + " 'images\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'var,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'EofScaling:\\n'\n", + " ' '\n", + " 'output_filename '\n", + " '+= '\n", + " '\"_EOFscaled\"\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Diagnostics '\n", + " 'results '\n", + " '-- '\n", + " 'data '\n", + " 'to '\n", + " 'NetCDF\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'global '\n", + " 'map, '\n", + " 'pc '\n", + " 'timeseries, '\n", + " 'and '\n", + " 'fraction '\n", + " 'in '\n", + " 'NetCDF '\n", + " 'output\\n'\n", + " ' '\n", + " 'output_nc_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"diagnostic_results\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'nc_out_model:\\n'\n", + " ' '\n", + " 'write_nc_output(\\n'\n", + " ' '\n", + " 'output_nc_file, '\n", + " 'eof_lr, '\n", + " 'pc, '\n", + " 'frac, '\n", + " 'slope, '\n", + " 'intercept\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Graphics '\n", + " '-- '\n", + " 'plot '\n", + " 'map '\n", + " 'image '\n", + " 'to '\n", + " 'PNG\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '= '\n", + " 'os.path.join(\\n'\n", + " ' '\n", + " 'outdir(output_type=\"graphics\"), '\n", + " 'output_filename\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'if '\n", + " 'plot_model:\\n'\n", + " ' '\n", + " '# '\n", + " 'plot_map(mode,\\n'\n", + " ' '\n", + " '# '\n", + " \"mip.upper()+' \"\n", + " \"'+model+' \"\n", + " \"('+run+')',\\n\"\n", + " ' '\n", + " '# '\n", + " 'msyear, '\n", + " 'meyear, '\n", + " 'season,\\n'\n", + " ' '\n", + " '# '\n", + " 'eof, '\n", + " 'frac,\\n'\n", + " ' '\n", + " '# '\n", + " \"output_img_file+'_org_eof')\\n\"\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(region_subdomain),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file,\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'plot_map(\\n'\n", + " ' '\n", + " 'mode '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " 'mip.upper()\\n'\n", + " ' '\n", + " '+ \" '\n", + " '\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'model\\n'\n", + " ' '\n", + " '+ \" '\n", + " '(\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'run\\n'\n", + " ' '\n", + " '+ \") '\n", + " '- '\n", + " 'EOF\"\\n'\n", + " ' '\n", + " '+ '\n", + " 'str(n '\n", + " '+ '\n", + " '1),\\n'\n", + " ' '\n", + " 'msyear,\\n'\n", + " ' '\n", + " 'meyear,\\n'\n", + " ' '\n", + " 'season,\\n'\n", + " ' '\n", + " 'eof_lr(longitude=(lon1g, '\n", + " 'lon2g)),\\n'\n", + " ' '\n", + " 'frac,\\n'\n", + " ' '\n", + " 'output_img_file '\n", + " '+ '\n", + " '\"_teleconnection\",\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " '# EOF '\n", + " 'swap '\n", + " 'diagnosis\\n'\n", + " ' '\n", + " '# - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- - - '\n", + " '- -\\n'\n", + " ' '\n", + " 'rms_list.append(dict_head[\"rms\"])\\n'\n", + " ' '\n", + " 'cor_list.append(dict_head[\"cor\"])\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'tcor_list.append(dict_head[\"tcor_cbf_vs_eof_pc\"])\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Find '\n", + " 'best '\n", + " 'matching '\n", + " 'eofs '\n", + " 'with '\n", + " 'different '\n", + " 'criteria\\n'\n", + " ' '\n", + " 'best_matching_eofs_rms '\n", + " '= '\n", + " 'rms_list.index(min(rms_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'best_matching_eofs_cor '\n", + " '= '\n", + " 'cor_list.index(max(cor_list)) '\n", + " '+ 1\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'best_matching_eofs_tcor '\n", + " '= '\n", + " 'tcor_list.index(max(tcor_list)) '\n", + " '+ 1\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " 'Save '\n", + " 'the '\n", + " 'best '\n", + " 'matching '\n", + " 'information '\n", + " 'to '\n", + " 'JSON\\n'\n", + " ' '\n", + " 'dict_head '\n", + " '= '\n", + " 'result_dict[\"RESULTS\"][model][run][\"defaultReference\"][\\n'\n", + " ' '\n", + " 'mode\\n'\n", + " ' '\n", + " '][season]\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__rms\"] '\n", + " '= '\n", + " 'best_matching_eofs_rms\\n'\n", + " ' '\n", + " 'dict_head[\"best_matching_model_eofs__cor\"] '\n", + " '= '\n", + " 'best_matching_eofs_cor\\n'\n", + " ' '\n", + " 'if '\n", + " 'CBF:\\n'\n", + " ' '\n", + " 'dict_head[\\n'\n", + " ' '\n", + " '\"best_matching_model_eofs__tcor_cbf_vs_eof_pc\"\\n'\n", + " ' '\n", + " '] = '\n", + " 'best_matching_eofs_tcor\\n'\n", + " '\\n'\n", + " ' '\n", + " 'debug_print(\"conventional '\n", + " 'eof '\n", + " 'end\", '\n", + " 'debug)\\n'\n", + " '\\n'\n", + " ' '\n", + " '# '\n", + " '=================================================================\\n'\n", + " ' '\n", + " '# '\n", + " 'Dictionary '\n", + " 'to '\n", + " 'JSON: '\n", + " 'individual '\n", + " 'JSON '\n", + " 'during '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " ' '\n", + " '# '\n", + " '-----------------------------------------------------------------\\n'\n", + " ' '\n", + " 'json_filename_tmp '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " 'model,\\n'\n", + " ' '\n", + " 'run,\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'variability_metrics_to_json(\\n'\n", + " ' '\n", + " 'outdir,\\n'\n", + " ' '\n", + " 'json_filename_tmp,\\n'\n", + " ' '\n", + " 'result_dict,\\n'\n", + " ' '\n", + " 'model=model,\\n'\n", + " ' '\n", + " 'run=run,\\n'\n", + " ' '\n", + " 'cmec_flag=cmec,\\n'\n", + " ' '\n", + " ')\\n'\n", + " '\\n'\n", + " ' '\n", + " 'except '\n", + " 'Exception '\n", + " 'as '\n", + " 'err:\\n'\n", + " ' '\n", + " 'if '\n", + " 'debug:\\n'\n", + " ' '\n", + " 'raise\\n'\n", + " ' '\n", + " 'else:\\n'\n", + " ' '\n", + " 'print(\"warning: '\n", + " 'failed '\n", + " 'for '\n", + " '\", '\n", + " 'model, '\n", + " 'run, '\n", + " 'err)\\n'\n", + " ' '\n", + " 'pass\\n'\n", + " '\\n'\n", + " '# '\n", + " '========================================================================\\n'\n", + " '# '\n", + " 'Dictionary '\n", + " 'to '\n", + " 'JSON: '\n", + " 'collective '\n", + " 'JSON '\n", + " 'at '\n", + " 'the '\n", + " 'end '\n", + " 'of '\n", + " 'model_realization '\n", + " 'loop\\n'\n", + " '# '\n", + " '------------------------------------------------------------------------\\n'\n", + " 'if '\n", + " 'not '\n", + " 'parallel '\n", + " 'and '\n", + " '(len(models) '\n", + " '> '\n", + " '1):\\n'\n", + " ' '\n", + " 'json_filename_all '\n", + " '= '\n", + " '\"_\".join(\\n'\n", + " ' '\n", + " '[\\n'\n", + " ' '\n", + " '\"var\",\\n'\n", + " ' '\n", + " '\"mode\",\\n'\n", + " ' '\n", + " 'mode,\\n'\n", + " ' '\n", + " '\"EOF\" '\n", + " '+ '\n", + " 'str(eofn_mod),\\n'\n", + " ' '\n", + " '\"stat\",\\n'\n", + " ' '\n", + " 'mip,\\n'\n", + " ' '\n", + " 'exp,\\n'\n", + " ' '\n", + " 'fq,\\n'\n", + " ' '\n", + " 'realm,\\n'\n", + " ' '\n", + " '\"allModels\",\\n'\n", + " ' '\n", + " '\"allRuns\",\\n'\n", + " ' '\n", + " 'str(msyear) '\n", + " '+ \"-\" '\n", + " '+ '\n", + " 'str(meyear),\\n'\n", + " ' '\n", + " ']\\n'\n", + " ' '\n", + " ')\\n'\n", + " ' '\n", + " 'variability_metrics_to_json(outdir, '\n", + " 'json_filename_all, '\n", + " 'result_dict, '\n", + " 'cmec_flag=cmec)\\n'\n", + " '\\n'\n", + " 'if '\n", + " 'not '\n", + " 'debug:\\n'\n", + " ' '\n", + " 'sys.exit(0)\\n',\n", + " 'userId': 'lee1043'}}}}\n" + ] + } + ], + "source": [ + "import pprint\n", + "\n", + "pprint.pprint(results_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load dictionary from remote JSON\n", + "\n", + "### Usage examples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json\"\n", + "json_data = load_json_from_url(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'DISCLAIMER': 'USER-NOTICE: The results in this file were produced with the PMP v1.1 (https://github.com/PCMDI/pcmdi_metrics). They are for research purposes only. They are subject to ongoing quality control and change as the PMP software advances, interpolation methods are modified, observational data sets are updated, problems with model data are corrected, etc. Use of these results for research (presentation, publications, etc.) should reference: Gleckler, P. J., C. Doutriaux, P. J. Durack, K. E. Taylor, Y. Zhang, and D. N. Williams, E. Mason, and J. Servonnat (2016), A more powerful reality test for climate models, Eos, 97, doi:10.1029/2016EO051663. If any problems are uncovered in using these results please contact the PMP development team at pcmdi-metrics@llnl.gov\\n',\n", + " 'REFERENCE': 'MC for ENSO Performance...',\n", + " 'RESULTS': {'model': {'ACCESS1-0': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0889669420569574,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.942498664900333, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5583900778200923,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4018762045511155, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6410299222206866,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4977522285851384, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6843057377081816, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.875566240338603,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.991165811567937, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 0.6631712431799373,\n", + " 'value_error': 0.05309619341351392},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 26.23107035339744,\n", + " 'value_error': 17.57013316804312},\n", + " 'HadISST': {'value': 13.489486763021702,\n", + " 'value_error': 13.966510108146199},\n", + " 'Tropflux': {'value': 26.638641393042768,\n", + " 'value_error': 17.473058729301606}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 1.6578289001548534,\n", + " 'value_error': 0.2658925622150123},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 18.999301115812486,\n", + " 'value_error': 38.76920628068375},\n", + " 'HadISST': {'value': 0.3700567420890543,\n", + " 'value_error': 32.221769654960866},\n", + " 'Tropflux': {'value': 19.2568577166379,\n", + " 'value_error': 38.645932467940106}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 10.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.92481203007519,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 79.59183673469387, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16212545625183172,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1455960395442026, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16019423631632693, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': -0.3337880808044643,\n", + " 'value_error': -0.026724434570681056},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 185.3719198588922,\n", + " 'value_error': -20.333709705673133},\n", + " 'HadISST': {'value': 185.60883980347452,\n", + " 'value_error': -13.820941313648582},\n", + " 'Tropflux': {'value': 183.982955205023,\n", + " 'value_error': -20.002888938026135}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10601628603226086,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07572287463897424, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10376934721294129, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1489899987227346,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5571217504386006, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.207558416822313,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4114280679640976, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28914994750737416,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30785834901792974, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29129989795013433, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.231386861156252,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.8841622478188085, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1038352764425874,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9410679210267212, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5213014940563446,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.345655306892618, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6452754179898839,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.500244791519785, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6886707956983943, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.861006398881714,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.983097397774121, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 0.6382166880857756,\n", + " 'value_error': 0.0510982299953862},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 29.006930793721146,\n", + " 'value_error': 16.908984391369277},\n", + " 'HadISST': {'value': 16.744801873557375,\n", + " 'value_error': 13.44096252213253},\n", + " 'Tropflux': {'value': 29.3991652908547,\n", + " 'value_error': 16.815562779035297}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 1.505678169870083,\n", + " 'value_error': 0.2414897136975784},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 26.433310432247215,\n", + " 'value_error': 35.211080923105555},\n", + " 'HadISST': {'value': 9.513804099554864,\n", + " 'value_error': 29.26454904937725},\n", + " 'Tropflux': {'value': 26.66722923492495,\n", + " 'value_error': 35.09912082351542}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 13.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 60.150375939849624,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 72.95918367346938, 'value_error': None},\n", + " 'Tropflux': {'value': 58.59375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17615616065044265,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15900144737120767, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17444989925127824, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': -0.3608750186672228,\n", + " 'value_error': -0.028893125246779373},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 192.29986013425773,\n", + " 'value_error': -21.983792386844687},\n", + " 'HadISST': {'value': 192.5560061572605,\n", + " 'value_error': -14.942512154840298},\n", + " 'Tropflux': {'value': 190.79818085264552,\n", + " 'value_error': -21.62612547910974}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13038831372373275,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10579917879545155, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1301831320827185, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.150780609667345,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.550059653557043, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1971069811038972,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4059223735646575, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2933263103451321,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3114345133049723, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29581441010978604, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.035541714101402,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.6861616009416527, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-0_r3i1p1',\n", + " 'nyears': 171,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.10686138895125,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9528760222410133, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6117609156571908,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4823304668585255, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5539343287369926,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4398602867066415, 'value_error': None},\n", + " 'Tropflux': {'value': 0.594150067495504, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.868830612217869,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.959821528352877, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 0.6187045752673538,\n", + " 'value_error': 0.04731352139467806},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 31.1773923337252,\n", + " 'value_error': 16.14480608509442},\n", + " 'HadISST': {'value': 19.29015182897076,\n", + " 'value_error': 12.740110644655047},\n", + " 'Tropflux': {'value': 31.557635098423333,\n", + " 'value_error': 16.055606534111412}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 1.4492085054615167,\n", + " 'value_error': 0.22197296279351864},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.192390263960938,\n", + " 'value_error': 33.37944695584852},\n", + " 'HadISST': {'value': 12.90744107879513,\n", + " 'value_error': 27.538398375860268},\n", + " 'Tropflux': {'value': 29.417536065508422,\n", + " 'value_error': 33.273310872903444}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 51.8796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 67.3469387755102, 'value_error': None},\n", + " 'Tropflux': {'value': 50.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14808294998201088,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13271032711104766, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14605727181265188, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': -0.5689351769109168,\n", + " 'value_error': -0.04350756037859862},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 245.51474759398846,\n", + " 'value_error': -34.13569264649697},\n", + " 'HadISST': {'value': 245.9185729500641,\n", + " 'value_error': -23.033357224939987},\n", + " 'Tropflux': {'value': 243.14728483388168,\n", + " 'value_error': -33.580319514444966}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12643247560494073,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10727364210692163, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12814726397905396, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.232920787383002,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6539934939454077, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2617076061502304,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4742314621397006, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2944719918775654,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31387311066471457, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29621592092902843, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.399485627465052,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.047163646529721, 'value_error': None}}}}}},\n", + " 'ACCESS1-3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9107871420709011,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.728007918250075, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3705518964847636,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8605742744825455, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6128484551635742,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5039427963116134, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6523802448706411, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.120794771689747,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.311951875912468, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 0.6591704259926121,\n", + " 'value_error': 0.052775871678554895},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 26.676107747065952,\n", + " 'value_error': 17.464135069535065},\n", + " 'HadISST': {'value': 14.01139230672893,\n", + " 'value_error': 13.882252151755198},\n", + " 'Tropflux': {'value': 27.081219968966636,\n", + " 'value_error': 17.367646266987755}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 18.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 38.46153846153847,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.46153846153847, 'value_error': None},\n", + " 'Tropflux': {'value': 38.46153846153847, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 1.0334523144346077,\n", + " 'value_error': 0.16575129302329406},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.50603181977032,\n", + " 'value_error': 24.167829355503983},\n", + " 'HadISST': {'value': 37.892990382023605,\n", + " 'value_error': 20.08630831685205},\n", + " 'Tropflux': {'value': 49.66658666665233,\n", + " 'value_error': 24.090983302767665}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 35.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.7669172932330826,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 27.55102040816326, 'value_error': None},\n", + " 'Tropflux': {'value': 10.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21925359895388835,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.214754688079866, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2179700449502751, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 0.08201445815258669,\n", + " 'value_error': 0.0065664118846491416},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 79.02342189152414,\n", + " 'value_error': 4.99615857978974},\n", + " 'HadISST': {'value': 78.96520872575252,\n", + " 'value_error': 3.395918183374591},\n", + " 'Tropflux': {'value': 79.36470185336589,\n", + " 'value_error': 4.914873214719781}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15841754859234353,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12974274015469492, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15540989176196593, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.151869385536187,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5076223309541772, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6069410137244964,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7415854270014426, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24255193766308267,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2570697479126406, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24682259501634465, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.4921024277714734,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.102081356651636, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9495119516960886,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7695441896741835, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2349285256091422,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8361593578621309, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5462503814795701,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4723779287845717, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5822194551911266, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.184206240117672,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.338571667211002, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 0.6192250865650893,\n", + " 'value_error': 0.04957768495073151},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 31.119492414665384,\n", + " 'value_error': 16.40581877430658},\n", + " 'HadISST': {'value': 19.222251267877628,\n", + " 'value_error': 13.04099585087477},\n", + " 'Tropflux': {'value': 31.50005507461372,\n", + " 'value_error': 16.31517713634179}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 1.1380478083150416,\n", + " 'value_error': 0.18252694692908336},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.3955477983731,\n", + " 'value_error': 26.613850339878038},\n", + " 'HadISST': {'value': 31.607152851161757,\n", + " 'value_error': 22.119239405486944},\n", + " 'Tropflux': {'value': 44.57235236792732,\n", + " 'value_error': 26.529226714121222}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 26.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.804511278195488,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 46.93877551020408, 'value_error': None},\n", + " 'Tropflux': {'value': 18.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2077117606465408,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20128736282299983, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2063754698073488, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 0.011419249875227251,\n", + " 'value_error': 0.0009142717001795556},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 97.07933463996979,\n", + " 'value_error': 0.695638117034614},\n", + " 'HadISST': {'value': 97.07122935340267,\n", + " 'value_error': 0.47282929333794843},\n", + " 'Tropflux': {'value': 97.12685261728092,\n", + " 'value_error': 0.6843203821395568}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13696100126612337,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11332078352954107, 'value_error': None},\n", + " 'Tropflux': {'value': 0.137808391694131, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1941598419280277,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5595903579789974, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6534802560577654,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8031489153627462, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24209693340280616,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25737165004739543, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2458176759601055, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.610861974173862,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.2078106811568006, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,\n", + " 'name': 'ACCESS1-3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"ACCESS1-3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9248077135258252,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7488022227444946, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2603677124277861,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8369974217757904, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5267402957372758,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4735648787989841, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5598225574393072, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.84409384183028,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.016411768045127, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 0.6934749618600298,\n", + " 'value_error': 0.05552243267634989},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.8601870192182,\n", + " 'value_error': 18.373003283675796},\n", + " 'HadISST': {'value': 9.536374677773308,\n", + " 'value_error': 14.604712077264292},\n", + " 'Tropflux': {'value': 23.286382084338968,\n", + " 'value_error': 18.27149301254121}}},\n", + " 'EnsoDuration': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 17.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.76923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.76923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 1.0181503979152642,\n", + " 'value_error': 0.16329707969057414},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 50.25367588136108,\n", + " 'value_error': 23.809985938747992},\n", + " 'HadISST': {'value': 38.81258411960237,\n", + " 'value_error': 19.788898355353812},\n", + " 'Tropflux': {'value': 50.41185345661725,\n", + " 'value_error': 23.73427771488621}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 23.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.82706766917293,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 53.06122448979592, 'value_error': None},\n", + " 'Tropflux': {'value': 28.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22323076184324586,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2194264804757086, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2219798413654614, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': -0.3469555707759648,\n", + " 'value_error': -0.02777867750037274},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 188.7397270492521,\n", + " 'value_error': -21.135847151646523},\n", + " 'HadISST': {'value': 188.98599316637245,\n", + " 'value_error': -14.366158823229977},\n", + " 'Tropflux': {'value': 187.29596961157077,\n", + " 'value_error': -20.791975950533306}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14477833009302135,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11261624701816798, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14291075928724745, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1958860051449434,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5548480855904452, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5988644270110819,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7457963536765899, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24068617429186429,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25372335679478264, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24543567702415445, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2245120268391583,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.85126838352652, 'value_error': None}}}}}},\n", + " 'BCC-CSM1-1': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.276702651909622,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8141163731575745, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3415100905569088,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5797789808307442, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9702647848312935,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7929711815579906, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0172856132875816, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.874120675472972,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.134235696528192, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 0.7323037767880102,\n", + " 'value_error': 0.05735845857348575},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.541000766586983,\n", + " 'value_error': 19.260159914287932},\n", + " 'HadISST': {'value': 4.471166042223638,\n", + " 'value_error': 15.25642200272976},\n", + " 'Tropflux': {'value': 18.991059201284077,\n", + " 'value_error': 19.153748130389108}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 1.753434087420725,\n", + " 'value_error': 0.2751023498536092},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.328079022407145,\n", + " 'value_error': 40.705771176207314},\n", + " 'HadISST': {'value': 5.375493586760336,\n", + " 'value_error': 33.71193540321589},\n", + " 'Tropflux': {'value': 14.600488631918301,\n", + " 'value_error': 40.57633969965783}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 54.88721804511278,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.38775510204081, 'value_error': None},\n", + " 'Tropflux': {'value': 53.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10574802405746656,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13210196179085845, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10769022982754567, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': -0.12450828493139014,\n", + " 'value_error': -0.009752241528258945},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 131.84508954697398,\n", + " 'value_error': -7.529450679869836},\n", + " 'HadISST': {'value': 131.9334644700536,\n", + " 'value_error': -5.099930458479712},\n", + " 'Tropflux': {'value': 131.326983548501,\n", + " 'value_error': -7.406949734890779}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29538085335098035,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2899595522336866, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2958324916017902, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.344591839483092,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2121116472259865, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9478284171471159,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7192197880614583, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2297360319302135,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23256497957111982, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23785523976797376, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5825241079131263,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.3265977091693286, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3133147011564037,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8341500678400728, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3566235979797434,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6269206023838659, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9941208166064649,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8154983170057336, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0413445070796736, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.011005975968729,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.273109938728947, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 0.6993850055298376,\n", + " 'value_error': 0.05478006141461307},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.20277371885271,\n", + " 'value_error': 18.39437058107546},\n", + " 'HadISST': {'value': 8.765411044494495,\n", + " 'value_error': 14.570610073247664},\n", + " 'Tropflux': {'value': 22.63260097199072,\n", + " 'value_error': 18.292742256287966}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 1.6549251570743164,\n", + " 'value_error': 0.25964694242524883},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 19.141176564407722,\n", + " 'value_error': 38.41889765968276},\n", + " 'HadISST': {'value': 0.54456193880895,\n", + " 'value_error': 31.817979582291333},\n", + " 'Tropflux': {'value': 19.39828204618374,\n", + " 'value_error': 38.29673771754653}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 11.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 65.41353383458647,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 76.53061224489795, 'value_error': None},\n", + " 'Tropflux': {'value': 64.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1152951502070794,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14142744496370552, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1175334629085968, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': -0.1890737928160853,\n", + " 'value_error': -0.01480940240420568},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 148.35880493038783,\n", + " 'value_error': -11.433952356255839},\n", + " 'HadISST': {'value': 148.4930079025491,\n", + " 'value_error': -7.744570535321325},\n", + " 'Tropflux': {'value': 147.57202783948156,\n", + " 'value_error': -11.247926837524231}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1857452264301395,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18128748613457202, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18630836529055697, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3477400019467243,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2151298068864804, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9355564118075479,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7284579593858195, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2283663213442194,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23191416317952995, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23647265516424223, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.6745673162528254,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.402865501720456, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2996568243264677,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8416447508785367, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3376563258086374,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5653366334925116, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9661411567381128,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7898696546521404, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0130691724558445, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.027451956033936,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.270436182737035, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 0.771612470968727,\n", + " 'value_error': 0.06043735312272848},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 14.16843436637242,\n", + " 'value_error': 20.29400919916143},\n", + " 'HadISST': {'value': 0.6566427148978611,\n", + " 'value_error': 16.075358140718393},\n", + " 'Tropflux': {'value': 14.642651094299353,\n", + " 'value_error': 20.18188543015057}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 1.9621947409552742,\n", + " 'value_error': 0.30785553216958045},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 4.128136896751886,\n", + " 'value_error': 45.55212579788172},\n", + " 'HadISST': {'value': 17.921307008271313,\n", + " 'value_error': 37.72561673699345},\n", + " 'Tropflux': {'value': 4.432979095841754,\n", + " 'value_error': 45.40728444660339}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 9.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 71.42857142857143,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 80.61224489795919, 'value_error': None},\n", + " 'Tropflux': {'value': 70.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11972252486076104,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14598948371177253, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12160956625593047, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': -0.34190702054468136,\n", + " 'value_error': -0.026780224676586892},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 187.44847535233725,\n", + " 'value_error': -20.67631121664731},\n", + " 'HadISST': {'value': 187.6911580513855,\n", + " 'value_error': -14.00470682737884},\n", + " 'Tropflux': {'value': 186.02572602797028,\n", + " 'value_error': -20.33991646882167}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2878272189144481,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2810314054343857, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2880038894268556, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3751864189658647,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.241830730475438, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.95058202083283,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7292632311757616, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23891424608447084,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24195389402069192, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2471036543473083, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.644967118860692,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.3839802463464195, 'value_error': None}}}}}},\n", + " 'BCC-CSM1-1-M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.780688107722851,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.751795754598107, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2713483733861755,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.34269942569315065, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.600408930033341,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5278584544618383, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6315151545169999, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.546526519644041,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.549585682328334, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 1.312158136951983,\n", + " 'value_error': 0.102776157280437},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 45.96004016381071,\n", + " 'value_error': 34.51078138826157},\n", + " 'HadISST': {'value': 71.17068184603616,\n", + " 'value_error': 27.336795065376602},\n", + " 'Tropflux': {'value': 45.15361548608763,\n", + " 'value_error': 34.32011039551748}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 1.5214821208312443,\n", + " 'value_error': 0.23871060207152045},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 25.661137216503466,\n", + " 'value_error': 35.32103288259321},\n", + " 'HadISST': {'value': 8.564039779868894,\n", + " 'value_error': 29.25237244022143},\n", + " 'Tropflux': {'value': 25.89751128575834,\n", + " 'value_error': 35.208723170551224}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 8.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.6734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12866610770126607,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13899746346122327, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12997275447855933, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 0.24874897679638486,\n", + " 'value_error': 0.019483523549958037},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 36.37826233680668,\n", + " 'value_error': 15.04271907277933},\n", + " 'HadISST': {'value': 36.20170242593306,\n", + " 'value_error': 10.188900152135302},\n", + " 'Tropflux': {'value': 37.413360820921525,\n", + " 'value_error': 14.797980461714841}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2826988437473747,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2816535862306858, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2842488661826046, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3283452004810337,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4053618568641348, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5262106909915965,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2833454936946764, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20599979701714036,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22267276930606786, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19980458639718712, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9484858853592688,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.864812325717921, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7777838657014953,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.7330237678295206, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.233023689835968,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3548696096696779, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6003190305111286,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5379191671861073, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6290620847164385, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.263823227930281,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.268029429709042, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 1.3184571343707698,\n", + " 'value_error': 0.1032695328357082},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 46.660719365765395,\n", + " 'value_error': 34.67644992832789},\n", + " 'HadISST': {'value': 71.99238439297514,\n", + " 'value_error': 27.468024980967947},\n", + " 'Tropflux': {'value': 45.8504234572658,\n", + " 'value_error': 34.4848636220577}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 1.6679654023827126,\n", + " 'value_error': 0.26169287169783656},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 18.50403663790344,\n", + " 'value_error': 38.72162545847104},\n", + " 'HadISST': {'value': 0.23911296275863553,\n", + " 'value_error': 32.06869439993549},\n", + " 'Tropflux': {'value': 18.763168023109692,\n", + " 'value_error': 38.59850293769157}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 7.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 78.94736842105263,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 85.71428571428571, 'value_error': None},\n", + " 'Tropflux': {'value': 78.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11073988925085478,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12680696561284865, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11279525935572005, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 0.057893872240628745,\n", + " 'value_error': 0.004534598042274018},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 85.19266772697236,\n", + " 'value_error': 3.5010445766135634},\n", + " 'HadISST': {'value': 85.15157514820247,\n", + " 'value_error': 2.3713660706354984},\n", + " 'Tropflux': {'value': 85.43357669539343,\n", + " 'value_error': 3.4440840774637924}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.27927431185720963,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2767563381954998, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28013201756878, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3155297166581075,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3812601124991835, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5364530684152913,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2605051256294458, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19008928763136193,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2064215110267346, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18402381926659445, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.99607950935536,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9290907290268442, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'BCC-CSM1-1-M_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BCC-CSM1-1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7347312369784347,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.70121612036843, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2469565909142823,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3270212246356474, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6071403990085729,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.536849699810774, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6374503568997398, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.651267339566656,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.661901384493073, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 1.3371681481538449,\n", + " 'value_error': 0.10473509254325442},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 48.742069354286784,\n", + " 'value_error': 35.168564169771706},\n", + " 'HadISST': {'value': 74.43323119115091,\n", + " 'value_error': 27.85784015251543},\n", + " 'Tropflux': {'value': 47.92027405189938,\n", + " 'value_error': 34.97425894763855}}},\n", + " 'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 1.7599211715097454,\n", + " 'value_error': 0.2761201309549294},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.011123307084498,\n", + " 'value_error': 40.85636808910122},\n", + " 'HadISST': {'value': 5.765345530853012,\n", + " 'value_error': 33.836657604827685},\n", + " 'Tropflux': {'value': 14.284540735512962,\n", + " 'value_error': 40.72645776200458}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 8.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.6734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12005755198436213,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13202972290204107, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12164668522324273, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 0.2887401516863427,\n", + " 'value_error': 0.022615874113943585},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 26.14984624255901,\n", + " 'value_error': 17.461125037731083},\n", + " 'HadISST': {'value': 25.94490093543075,\n", + " 'value_error': 11.82696151491186},\n", + " 'Tropflux': {'value': 27.35135668558797,\n", + " 'value_error': 17.1770399950814}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.310091002428269,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3063216866582536, 'value_error': None},\n", + " 'Tropflux': {'value': 0.31068599253119716, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3229982127439062,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.395505785098484, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5230555229633508,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2904841181432017, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19311035116652037,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2091594859123878, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18727130592225386, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8833384662660486,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.815183974862213, 'value_error': None}}}}}},\n", + " 'BNU-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'BNU-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 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37.24327233399897},\n", + " 'HadISST': {'value': 83.37565079466324,\n", + " 'value_error': 29.60470103091263},\n", + " 'Tropflux': {'value': 55.50349170719284,\n", + " 'value_error': 37.03750441385046}}},\n", + " 'EnsoDuration': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 1.4401619095162679,\n", + " 'value_error': 0.2309818221228842},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.634402460768193,\n", + " 'value_error': 33.67894849848756},\n", + " 'HadISST': {'value': 13.451111080336423,\n", + " 'value_error': 27.99116682665337},\n", + " 'Tropflux': {'value': 29.85814280334432,\n", + " 'value_error': 33.57186009537332}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 19.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.224489795918366, 'value_error': None},\n", + " 'Tropflux': {'value': 40.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15034880062081318,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17932376255417704, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15264254880091654, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': -0.05865445309404007,\n", + " 'value_error': -0.004696114643197858},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 115.00186362809323,\n", + " 'value_error': -3.5731132738017175},\n", + " 'HadISST': {'value': 115.04349606069255,\n", + " 'value_error': -2.4286659728624995},\n", + " 'Tropflux': {'value': 114.75778971765492,\n", + " 'value_error': -3.5149802477460255}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1521343947748536,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15261224417799274, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15416906265054214, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.416862234472185,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6124292011423593, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5273038798051564,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6117639058883786, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10379928368522905,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11436747761796162, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09965425710627636, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5548554750224184,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7222012976261847, 'value_error': None}}}}}},\n", + " 'CCSM4': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3348754624608516,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4208887895278357, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6173120412414144,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.69642220474339, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.39568402604886177,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2505450419980529, 'value_error': None},\n", + " 'Tropflux': {'value': 0.44407893912620916, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.313354638607431,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.611776902275294, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i1p1': {'value': 1.1442037549863202,\n", + " 'value_error': 0.09160961743140386},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 27.277361874484036,\n", + " 'value_error': 30.314662394113935},\n", + " 'HadISST': {'value': 49.261077149400315,\n", + " 'value_error': 24.09714455223943},\n", + " 'Tropflux': {'value': 26.57415841266063,\n", + " 'value_error': 30.1471748281745}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i1p1': {'value': 1.421393172625417,\n", + " 'value_error': 0.22797157930410988},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.55143364848024,\n", + " 'value_error': 33.24003165243589},\n", + " 'HadISST': {'value': 14.579049066749539,\n", + " 'value_error': 27.62637531122311},\n", + " 'Tropflux': {'value': 30.772258121948916,\n", + " 'value_error': 33.13433886605671}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i1p1': {'value': 34.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2556390977443606,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.612244897959183, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1060228407271465,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12650936562063525, 'value_error': None},\n", + " 'Tropflux': {'value': 0.108069557359399, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i1p1': {'value': 0.2504174234350832,\n", + " 'value_error': 0.02004943984764332},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 35.95152902630469,\n", + " 'value_error': 15.254934151931252},\n", + " 'HadISST': {'value': 35.773784866178595,\n", + " 'value_error': 10.368867890279333},\n", + " 'Tropflux': {'value': 36.9935702790242,\n", + " 'value_error': 15.006742892215454}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13538229642648042,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14161183302509184, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13405984765792123, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2933035425166104,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5756156317947019, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.332514734772599,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3746835130969084, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2025922721005593,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20513700529514345, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2111573666242317, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5963288040131582,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7058608295075344, 'value_error': None}}}}},\n", + " 'r1i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3453065342349928,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4111485834038684, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6399935282363962,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.716689533952502, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.454898740158078,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2913319220719114, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5036638463027653, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.552460344239443,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.820575895270259, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p1': {'value': 1.1486935396316083,\n", + " 'value_error': 0.09196908789452006},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 27.77679035699558,\n", + " 'value_error': 30.433615251199956},\n", + " 'HadISST': {'value': 49.84676836864696,\n", + " 'value_error': 24.191700254521},\n", + " 'Tropflux': {'value': 27.070827568355032,\n", + " 'value_error': 30.26547047442849}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p1': {'value': 1.419448326506585,\n", + " 'value_error': 0.2276596531954494},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.646457866499894,\n", + " 'value_error': 33.19455039658485},\n", + " 'HadISST': {'value': 14.695927779894488,\n", + " 'value_error': 27.588575039042134},\n", + " 'Tropflux': {'value': 30.866980193012722,\n", + " 'value_error': 33.08900222621294}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p1': {'value': 38.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.789473684210526,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 21.428571428571427, 'value_error': None},\n", + " 'Tropflux': {'value': 20.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10893956686029498,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1295152776427591, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11093774758504696, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p1': {'value': 0.19654243108866398,\n", + " 'value_error': 0.01573598831729566},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.730965122140915,\n", + " 'value_error': 11.972976173901497},\n", + " 'HadISST': {'value': 49.59146097397188,\n", + " 'value_error': 8.138101873414511},\n", + " 'Tropflux': {'value': 50.54882083798834,\n", + " 'value_error': 11.778180967999441}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13000281604544192,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13682820290760545, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13022156234718607, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2490236771070917,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5154959738386062, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.35759176120269104,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.35578289845702293, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21077219604409586,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21260868265937913, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2194647823599543, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5255776372203644,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.6956852305863532, 'value_error': None}}}}},\n", + " 'r1i2p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,\n", + " 'name': 'CCSM4_r1i2p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r1i2p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.31114971581617,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.356881412896999, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6497824806308932,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6965941782957846, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.48860050735628147,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3233089475320854, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5369394231421579, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.410974983866879,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.762180078151508, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p2': {'value': 1.063525045687739,\n", + " 'value_error': 0.08515015104572429},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.3029346938246,\n", + " 'value_error': 28.177151636854976},\n", + " 'HadISST': {'value': 38.73656086420987,\n", + " 'value_error': 22.39803588231617},\n", + " 'Tropflux': {'value': 17.649314662773,\n", + " 'value_error': 28.021473751302167}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p2': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p2': {'value': 1.541950821060864,\n", + " 'value_error': 0.2473073394866736},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.661046662107143,\n", + " 'value_error': 36.05933606947881},\n", + " 'HadISST': {'value': 7.3339397120936844,\n", + " 'value_error': 29.969548830316594},\n", + " 'Tropflux': {'value': 24.900600702982707,\n", + " 'value_error': 35.9446787868379}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p2': {'value': 38.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.285714285714285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 22.448979591836736, 'value_error': None},\n", + " 'Tropflux': {'value': 18.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12439932968805453,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14588601691637065, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12653087653953046, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p2': {'value': 0.27872539564992915,\n", + " 'value_error': 0.02231589151200781},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 28.711288663410183,\n", + " 'value_error': 16.97939983083047},\n", + " 'HadISST': {'value': 28.51345174505193,\n", + " 'value_error': 11.540997289707985},\n", + " 'Tropflux': {'value': 29.871125532840658,\n", + " 'value_error': 16.70315225144}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09512225600286646,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10654579914815948, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09566472805258172, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2796635863138115,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.539734321438176, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4065193302094096,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.33208772715002305, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2190396915329077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22139910374876795, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22772970184297928, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5545462756330466,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.6761697353487928, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.329368781296368,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3853311664163965, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5957092381976055,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6244351835227812, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3903391666865677,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2565446431284051, 'value_error': None},\n", + " 'Tropflux': {'value': 0.43769700561978336, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.4322668102796925,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.799759845991943, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r2i1p1': {'value': 0.9847609854294257,\n", + " 'value_error': 0.07884397926804612},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.541486606878879,\n", + " 'value_error': 26.09036780564044},\n", + " 'HadISST': {'value': 28.461810039819625,\n", + " 'value_error': 20.73925007839389},\n", + " 'Tropflux': {'value': 8.936273303737009,\n", + " 'value_error': 25.94621933578713}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r2i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r2i1p1': {'value': 1.5231859309267517,\n", + " 'value_error': 0.24429771363386318},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 25.57788989919036,\n", + " 'value_error': 35.62050918186948},\n", + " 'HadISST': {'value': 8.461646521359649,\n", + " 'value_error': 29.604832080930983},\n", + " 'Tropflux': {'value': 25.814528668582028,\n", + " 'value_error': 35.50724722992422}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r2i1p1': {'value': 38.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.037593984962406,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 21.93877551020408, 'value_error': None},\n", + " 'Tropflux': {'value': 19.53125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12456106988480453,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.144709275801582, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12660202689742858, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r2i1p1': {'value': 0.3113630623237867,\n", + " 'value_error': 0.024928996166503122},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 20.363656066864408,\n", + " 'value_error': 18.967621932761997},\n", + " 'HadISST': {'value': 20.142653209923115,\n", + " 'value_error': 12.892403471218872},\n", + " 'Tropflux': {'value': 21.65930535142216,\n", + " 'value_error': 18.65902682940593}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08676907479403038,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09904311772990587, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08602567620710229, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3226141317318787,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5941973588701739, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.41796568351469054,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3522139289469063, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20928229913296018,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21123113650386335, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2181360120635771, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5498462957133186,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7055017561344854, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3110428625742987,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3978225928404995, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5836880283146393,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6464882816829174, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3700964595990826,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2382847146612674, 'value_error': None},\n", + " 'Tropflux': {'value': 0.4181506475935778, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.36960402639509,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.674215766047146, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r3i1p1': {'value': 1.066530051670549,\n", + " 'value_error': 0.08539074407582459},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.637201411830315,\n", + " 'value_error': 28.256766601815713},\n", + " 'HadISST': {'value': 39.12856309972078,\n", + " 'value_error': 22.461321868953206},\n", + " 'Tropflux': {'value': 17.981734567568477,\n", + " 'value_error': 28.100648846060146}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r3i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r3i1p1': {'value': 1.4234673642108422,\n", + " 'value_error': 0.228304250616042},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.450089675031922,\n", + " 'value_error': 33.28853772048278},\n", + " 'HadISST': {'value': 14.45439712592341,\n", + " 'value_error': 27.66668955805497},\n", + " 'Tropflux': {'value': 30.671236390279688,\n", + " 'value_error': 33.18269070015034}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r3i1p1': {'value': 35.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.015037593984962,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 28.061224489795915, 'value_error': None},\n", + " 'Tropflux': {'value': 10.15625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1425117155059529,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16399332816876988, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14468615978775204, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r3i1p1': {'value': 0.19717460483302968,\n", + " 'value_error': 0.015786602724580327},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.56927604651302,\n", + " 'value_error': 12.011486948074442},\n", + " 'HadISST': {'value': 49.42932318678987,\n", + " 'value_error': 8.164277871670201},\n", + " 'Tropflux': {'value': 50.38976237452584,\n", + " 'value_error': 11.81606518833351}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14492781700592755,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15826743583929878, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14622657969162361, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3110819991310134,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5865881607124575, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.37895160142602374,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.352811134223505, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2139007274671339,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2156243809655798, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22266255384750783, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.630314876829397,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7813385269089885, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.329264803328015,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3807235509116127, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4931014555308098,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5152190659816651, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.37038587624953884,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2520765586777242, 'value_error': None},\n", + " 'Tropflux': {'value': 0.41617043532174125, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.713195003676942,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.067543889626098, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r4i1p1': {'value': 0.9353287701184664,\n", + " 'value_error': 0.07488623457992638},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 4.0428139019762455,\n", + " 'value_error': 24.780705158568637},\n", + " 'HadISST': {'value': 22.01339062934227,\n", + " 'value_error': 19.698198401457333},\n", + " 'Tropflux': {'value': 3.467980594338483,\n", + " 'value_error': 24.6437925340666}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r4i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r4i1p1': {'value': 1.4778298733718112,\n", + " 'value_error': 0.23702323654269403},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.793964404972254,\n", + " 'value_error': 34.55983377003307},\n", + " 'HadISST': {'value': 11.187393092781573,\n", + " 'value_error': 28.72328607889426},\n", + " 'Tropflux': {'value': 28.02355675841014,\n", + " 'value_error': 34.44994443039134}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r4i1p1': {'value': 38.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.789473684210526,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 21.428571428571427, 'value_error': None},\n", + " 'Tropflux': {'value': 20.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14974306785971686,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1718668167176778, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15203045711161578, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r4i1p1': {'value': 0.013691942609947189,\n", + " 'value_error': 0.0010962327460680248},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 96.49805521997138,\n", + " 'value_error': 0.8340860634280602},\n", + " 'HadISST': {'value': 96.48833679540525,\n", + " 'value_error': 0.5669331715675615},\n", + " 'Tropflux': {'value': 96.55503036504604,\n", + " 'value_error': 0.8205158396085497}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08366705951706803,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06665112849736267, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07946062199734603, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3357135612488062,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6120639553443352, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4532542094398217,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3978374227652189, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19295430086430593,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19539712921524688, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2016194135921754, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.592449415742742,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7291725843207841, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3856698199161777,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4529878361950073, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6531177625866311,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7236832076644217, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3912823558770787,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24258941629823105, 'value_error': None},\n", + " 'Tropflux': {'value': 0.43951151233474983, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.566591658180531,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.843076622155677, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r5i1p1': {'value': 1.1464367213282234,\n", + " 'value_error': 0.09178839782032279},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 27.525749509914714,\n", + " 'value_error': 30.373822854387917},\n", + " 'HadISST': {'value': 49.5523670179909,\n", + " 'value_error': 24.1441712400008},\n", + " 'Tropflux': {'value': 26.82117371413862,\n", + " 'value_error': 30.20600842874732}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r5i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r5i1p1': {'value': 1.4575592167680285,\n", + " 'value_error': 0.23377210681413332},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.784378645907736,\n", + " 'value_error': 34.085793736563055},\n", + " 'HadISST': {'value': 12.40559140446912,\n", + " 'value_error': 28.32930306425331},\n", + " 'Tropflux': {'value': 29.010821795340984,\n", + " 'value_error': 33.97741169428244}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r5i1p1': {'value': 30.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.774436090225564,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.775510204081634, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10671371264670343,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12807647264969876, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10882463237085656, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r5i1p1': {'value': 0.16279884428346003,\n", + " 'value_error': 0.01303433918835616},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 58.36145540670267,\n", + " 'value_error': 9.917383604893198},\n", + " 'HadISST': {'value': 58.2459021698323,\n", + " 'value_error': 6.740903591730114},\n", + " 'Tropflux': {'value': 59.03889673371322,\n", + " 'value_error': 9.756032011666449}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13435604825596462,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14778257556042165, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13485731834560458, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3041987162238182,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5796696382253237, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.344335995025295,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.36394419743300815, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20826598282309558,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20937046872093895, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21721057256151613, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.525962614699487,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.662001547746111, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CCSM4_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CCSM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.359649455091757,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4390935399042255, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5786596850945231,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6657213962415829, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3822096740525966,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2385134937894553, 'value_error': None},\n", + " 'Tropflux': {'value': 0.4304261899549294, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.435947940855175,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.710006525416257, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CCSM4_r6i1p1': {'value': 1.115440113924664,\n", + " 'value_error': 0.08930668306144625},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 24.077791573933883,\n", + " 'value_error': 29.55259526733719},\n", + " 'HadISST': {'value': 45.508867782063575,\n", + " 'value_error': 23.491376905095372},\n", + " 'Tropflux': {'value': 23.39226563840862,\n", + " 'value_error': 29.389318098547722}}},\n", + " 'EnsoDuration': {'diagnostic': {'CCSM4_r6i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CCSM4_r6i1p1': {'value': 1.4546999250384651,\n", + " 'value_error': 0.2333135164227952},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.92408222351262,\n", + " 'value_error': 34.018927686110125},\n", + " 'HadISST': {'value': 12.577425224441493,\n", + " 'value_error': 28.273729512918962},\n", + " 'Tropflux': {'value': 29.15008115975923,\n", + " 'value_error': 33.9107582567193}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r6i1p1': {'value': 27.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.293233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 43.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 14.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11282892307445538,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1346171924329988, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11497941487029303, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CCSM4_r6i1p1': {'value': 0.22271979926486726,\n", + " 'value_error': 0.0178318551360614},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 43.035662603640525,\n", + " 'value_error': 13.567649668744652},\n", + " 'HadISST': {'value': 42.877577981888756,\n", + " 'value_error': 9.222010766857057},\n", + " 'Tropflux': {'value': 43.962448030340525,\n", + " 'value_error': 13.346909806538033}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0982082580361651,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1023909128588462, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09767167628488198, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2859678666873833,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5630598325200815, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3342940750796701,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.380011932313065, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19698117966357842,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19763579409716256, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20585676316813692, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4661565740107017,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.6544641648030276, 'value_error': None}}}}}},\n", + " 'CESM1-BGC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-BGC_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-BGC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4485311550802424,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.44562851894086, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5776677798155143,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5687391928919707, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3662307523347744,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2412451492454894, 'value_error': None},\n", + " 'Tropflux': {'value': 0.41175932859836434, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.960517863572848,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.312574829922901, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 0.9318009358848852,\n", + " 'value_error': 0.07460378178854951},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 3.650389534879083,\n", + " 'value_error': 24.68723832339408},\n", + " 'HadISST': {'value': 21.55318558682766,\n", + " 'value_error': 19.62390155431584},\n", + " 'Tropflux': {'value': 3.0777243596521635,\n", + " 'value_error': 24.550842100246474}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 1.3463979366308163,\n", + " 'value_error': 0.215943392649467},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.215663731560475,\n", + " 'value_error': 31.486228365455155},\n", + " 'HadISST': {'value': 19.086010615108233,\n", + " 'value_error': 26.168758533513586},\n", + " 'Tropflux': {'value': 34.42483711241093,\n", + " 'value_error': 31.386112118776673}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 42.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 14.285714285714285, 'value_error': None},\n", + " 'Tropflux': {'value': 31.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1486286806122117,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16931495576996286, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1507140447879472, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 0.21679795096627305,\n", + " 'value_error': 0.017357727818477553},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 44.550274980288755,\n", + " 'value_error': 13.206902382818683},\n", + " 'HadISST': {'value': 44.396393636159566,\n", + " 'value_error': 8.976808728468075},\n", + " 'Tropflux': {'value': 45.45241831086448,\n", + " 'value_error': 12.992031724794876}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1371459234577554,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13301977430529827, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13405957526860807, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3462216561215763,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6094914267021792, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.47694982237274913,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.34926036040882036, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19350648189094052,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19066623268343252, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20353617073294764, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5922995707994436,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.8578148273000956, 'value_error': None}}}}}},\n", + " 'CESM1-CAM5': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1628270818985167,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6401876887172417, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.862376985515493,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': 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0.09098052940529824,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09894059040988966, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0878137267125927, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1224841362152684,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4594524812645753, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3358576788910401,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4346835827266837, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23433460371952902,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2591049909311961, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2256114676545575, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.004901485303239,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.0374116615883167, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.150807132862054,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6333602370524256, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9227765685241254,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9444034776108766, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0591605566787574,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8737274901128684, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1070393178866462, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.66381592602734,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.72181774590748, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 0.9442432036292067,\n", + " 'value_error': 0.0755999604700729},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 5.034425382805622,\n", + " 'value_error': 25.016885158096862},\n", + " 'HadISST': {'value': 23.176276122587055,\n", + " 'value_error': 19.885938034344836},\n", + " 'Tropflux': {'value': 4.454113452608173,\n", + " 'value_error': 24.878667646450438}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 1.546797349817112,\n", + " 'value_error': 0.24808465489523424},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.424247667722536,\n", + " 'value_error': 36.172674709599455},\n", + " 'HadISST': {'value': 7.042679627933178,\n", + " 'value_error': 30.063746568813844},\n", + " 'Tropflux': {'value': 24.664554654497493,\n", + " 'value_error': 36.0576570459335}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 41.75,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 25.563909774436087,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 14.795918367346939, 'value_error': None},\n", + " 'Tropflux': {'value': 30.46875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16378704610665498,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1827940829741964, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16586793812515968, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 0.8627084550711711,\n", + " 'value_error': 0.0690719560912928},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 120.65220816278968,\n", + " 'value_error': 52.55449279006228},\n", + " 'HadISST': {'value': 121.26455129641376,\n", + " 'value_error': 35.721595868822945},\n", + " 'Tropflux': {'value': 117.06229102794174,\n", + " 'value_error': 51.699453650634794}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.06851316926429511,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06960734227829767, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06627678745748232, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1150786016724001,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4469129418781546, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3564543061534498,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.43336323485077904, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.251094798239275,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27652499068936554, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24219973756428295, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.0624906575226643,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.076523981602271, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-CAM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-CAM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1626052908400413,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6469739420103926, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8349956953318936,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8538038151209277, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0760285211390308,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8877062786298223, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1240892886457496, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.608607792735975,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.611235688016729, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 1.0906233198867472,\n", + " 'value_error': 0.0873197493551199},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 21.317255208305344,\n", + " 'value_error': 28.89509634751176},\n", + " 'HadISST': {'value': 42.271523564871636,\n", + " 'value_error': 22.968730592628077},\n", + " 'Tropflux': {'value': 20.646981150256245,\n", + " 'value_error': 28.73545183978439}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 1.4714511760909985,\n", + " 'value_error': 0.23600018273814832},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.10562439453344,\n", + " 'value_error': 34.41066455802407},\n", + " 'HadISST': {'value': 11.570731354098763,\n", + " 'value_error': 28.599308921503724},\n", + " 'Tropflux': {'value': 28.33422576778683,\n", + " 'value_error': 34.301249529292264}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 48.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 45.86466165413533,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 51.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.205289744500677,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22494976767670877, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2073414566483469, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 0.4528252045214126,\n", + " 'value_error': 0.036255031998212395},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 15.817667836782329,\n", + " 'value_error': 27.585215847010318},\n", + " 'HadISST': {'value': 16.13907932068593,\n", + " 'value_error': 18.749832414470248},\n", + " 'Tropflux': {'value': 13.93336387378439,\n", + " 'value_error': 27.136416173251334}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13738249990248663,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13213562127400896, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1323602748143438, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1067417638395454,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4375954621553453, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.33661548914271094,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.45694474439320343, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23521571460678276,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25961485015699515, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22657331048616985, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.986855811267612,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.9907710535818026, 'value_error': None}}}}}},\n", + " 'CESM1-FASTCHEM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 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0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 65.16947950370448,\n", + " 'value_error': 8.295862314444639},\n", + " 'HadISST': {'value': 65.0728195597862,\n", + " 'value_error': 5.638746094720672},\n", + " 'Tropflux': {'value': 65.73615718794987,\n", + " 'value_error': 8.160892179683996}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0965354339526479,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11304951261808976, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0998994934595478, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.301233552472251,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5808837537267193, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4297374040762737,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3932194530045997, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19330767034122598,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19583057631216325, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20184254987821496, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.533026919554631,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.6770147912622382, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2875208129278142,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4018514711684857, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4823045614654373,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5616745086610696, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3256528891800055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21155256384523494, 'value_error': None},\n", + " 'Tropflux': {'value': 0.37236351998033096, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.740618342545036,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.99577057039031, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 1.0461106004179757,\n", + " 'value_error': 0.08375587956043083},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 16.36581060837746,\n", + " 'value_error': 27.71577137408879},\n", + " 'HadISST': {'value': 36.46485108560036,\n", + " 'value_error': 22.031284415950317},\n", + " 'Tropflux': {'value': 15.722893127589362,\n", + " 'value_error': 27.562642600123578}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 1.4671142997509485,\n", + " 'value_error': 0.23530460844700293},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.31752202431027,\n", + " 'value_error': 34.30924440940347},\n", + " 'HadISST': {'value': 11.831363041503536,\n", + " 'value_error': 28.515016851050568},\n", + " 'Tropflux': {'value': 28.545449630127084,\n", + " 'value_error': 34.20015186466372}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 31.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.263157894736842,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 35.714285714285715, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12123934631410288,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14452488590579468, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12368811066431441, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 0.19814729924026026,\n", + " 'value_error': 0.01586448060440351},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.32049307984403,\n", + " 'value_error': 12.070741567536258},\n", + " 'HadISST': {'value': 49.17984980989263,\n", + " 'value_error': 8.204553582792203},\n", + " 'Tropflux': {'value': 50.1450270004114,\n", + " 'value_error': 11.874355760458137}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1008459847257217,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10821054449636473, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10091159717064506, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3118928190282237,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6040994971293112, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.34877773074437984,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3945127208934276, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20377662412969746,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2059118447890375, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21238258059912057, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4680021820819977,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.581650716589518, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-FASTCHEM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-FASTCHEM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3573243483868578,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4669357895567536, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5236255250541135,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6293961409239661, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.32048651758016006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20326100610814518, 'value_error': None},\n", + " 'Tropflux': {'value': 0.36781234466942814, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.814081912873389,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.037570861932816, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 1.1700395004464879,\n", + " 'value_error': 0.09367813254276126},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 30.151242955454734,\n", + " 'value_error': 30.999157527006243},\n", + " 'HadISST': {'value': 52.63134331007067,\n", + " 'value_error': 24.64125017176344},\n", + " 'Tropflux': {'value': 29.43216139013166,\n", + " 'value_error': 30.827888146768007}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 1.452156379835357,\n", + " 'value_error': 0.23290556735694104},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.048358582234584,\n", + " 'value_error': 33.959445535295686},\n", + " 'HadISST': {'value': 12.730283739717665,\n", + " 'value_error': 28.22429285052643},\n", + " 'Tropflux': {'value': 29.273962358970774,\n", + " 'value_error': 33.851465240329425}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 34.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2556390977443606,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.612244897959183, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1353136317146382,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15659656932427646, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13741618543845324, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 0.04849621035108394,\n", + " 'value_error': 0.0038828043150311137},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 87.5962779330695,\n", + " 'value_error': 2.954293217204093},\n", + " 'HadISST': {'value': 87.56185573488573,\n", + " 'value_error': 2.0080503641151086},\n", + " 'Tropflux': {'value': 87.79808119057923,\n", + " 'value_error': 2.906228129027042}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1311966080432691,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13042986229935843, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12863766576540836, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3036522827326702,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.586408685240943, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3414491040152393,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3803877646251331, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2037561878559243,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20546024486536307, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2124465938439825, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4779730763190055,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.6053517814859262, 'value_error': None}}}}}},\n", + " 'CESM1-WACCM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6141773929126815,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6120839070315434, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.865912100083203,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8843067070012708, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6407187816134237,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4396512640665915, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6907192592914236, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.433034860026156,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.493288316239419, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 1.0495467443822926,\n", + " 'value_error': 0.0840309912550381},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 16.74803565954801,\n", + " 'value_error': 27.806808957003454},\n", + " 'HadISST': {'value': 36.91309515674526,\n", + " 'value_error': 22.1036502489146},\n", + " 'Tropflux': {'value': 16.103006397252035,\n", + " 'value_error': 27.653177203226914}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 1.5650028139360084,\n", + " 'value_error': 0.2510045566416899},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 23.53473770864059,\n", + " 'value_error': 36.59841912375169},\n", + " 'HadISST': {'value': 5.948592441384455,\n", + " 'value_error': 30.41759024426548},\n", + " 'Tropflux': {'value': 23.77787305572129,\n", + " 'value_error': 36.48204772751751}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 31.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.263157894736842,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 35.714285714285715, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14644938277763006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16256506514648122, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14616245206260317, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 0.32454281065209234,\n", + " 'value_error': 0.025984220550216756},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 16.99271359542362,\n", + " 'value_error': 19.770506133586977},\n", + " 'HadISST': {'value': 16.76235586506676,\n", + " 'value_error': 13.43812855443681},\n", + " 'Tropflux': {'value': 18.343206673476896,\n", + " 'value_error': 19.448848447382378}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.06470039922864781,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.03854520781493399, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06294393179276718, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2020328279027799,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2493829854385226, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6940914483336361,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.36339164330018836, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14930643716521624,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16863631656929096, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15290685124144188, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3515296308762297,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.3164535381005944, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r2i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.499697537088838,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5987203517927087, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7549366830473916,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8342343172523758, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4092434308868775,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.293909995232964, 'value_error': None},\n", + " 'Tropflux': {'value': 0.45582545906905714, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.739429771309716,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.724126975782613, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 1.2358443659005571,\n", + " 'value_error': 0.17305282525287746},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.471153973924906,\n", + " 'value_error': 40.98590986026902},\n", + " 'HadISST': {'value': 61.21557060048291,\n", + " 'value_error': 35.69423234759478},\n", + " 'Tropflux': {'value': 36.71163012811598,\n", + " 'value_error': 40.75946398430762}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 1.6116418305734659,\n", + " 'value_error': 0.4535955692423266},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 21.255978457578895,\n", + " 'value_error': 47.222112634868296},\n", + " 'HadISST': {'value': 3.145744342427665,\n", + " 'value_error': 43.04957312048569},\n", + " 'Tropflux': {'value': 21.506359538275326,\n", + " 'value_error': 47.07196125368797}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 17.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 47.368421052631575,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 64.28571428571429, 'value_error': None},\n", + " 'Tropflux': {'value': 45.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10626622921153701,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1168082874783333, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10427147364890162, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 0.367837424602116,\n", + " 'value_error': 0.05150754198304977},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 5.919387359342931,\n", + " 'value_error': 28.04937179781183},\n", + " 'HadISST': {'value': 5.658299479745772,\n", + " 'value_error': 20.887899139607487},\n", + " 'Tropflux': {'value': 7.450038723259992,\n", + " 'value_error': 27.593020505082432}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09783021382401647,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09651575167344903, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10051001444491907, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.174497728978548,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2782700665921358, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5326834862234044,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.24534527670807513, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15099148069332816,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1704296889257615, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15447418446341213, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3680545539374505,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.316365013053664, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r3i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5308199731160224,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.576303410285562, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8751503125360287,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9077078327927308, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4367645956831243,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28616163352847684, 'value_error': None},\n", + " 'Tropflux': {'value': 0.4857982252895094, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.233096958189007,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.291528401841891, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 1.141930180545983,\n", + " 'value_error': 0.15990220891690493},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 27.024457131311923,\n", + " 'value_error': 37.87131190461277},\n", + " 'HadISST': {'value': 48.96448996511149,\n", + " 'value_error': 32.98175912258758},\n", + " 'Tropflux': {'value': 26.32265096032085,\n", + " 'value_error': 37.662074085389406}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 1.4190925847208922,\n", + " 'value_error': 0.3994027063351872},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.66383923393819,\n", + " 'value_error': 41.580299421212885},\n", + " 'HadISST': {'value': 14.717306665206312,\n", + " 'value_error': 37.90626976276926},\n", + " 'Tropflux': {'value': 30.88430629320053,\n", + " 'value_error': 41.44808722146948}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 22.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 33.83458646616541,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 55.10204081632652, 'value_error': None},\n", + " 'Tropflux': {'value': 31.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12140113146311766,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13133638332786987, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11988967458746143, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 0.5391587826571417,\n", + " 'value_error': 0.07549733054835818},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 37.89893357872038,\n", + " 'value_error': 41.11344888074988},\n", + " 'HadISST': {'value': 38.28162390307334,\n", + " 'value_error': 30.616499353098042},\n", + " 'Tropflux': {'value': 35.65537685815371,\n", + " 'value_error': 40.444550636591785}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10695319614301474,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10676999912708521, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10942579946648617, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2075862524410002,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2902106401651665, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5950045852126716,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2864873877876307, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16075972310390169,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1815353643182361, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16358939181673665, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.427929663746014,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.373415152644087, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r4i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5445222741003195,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6591886753020026, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7885614492745099,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8651474834934925, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4180530926407542,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29413665486007246, 'value_error': None},\n", + " 'Tropflux': {'value': 0.4646027114842067, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.5969219450057786,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.554326591105699, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 1.2267790071625275,\n", + " 'value_error': 0.17178342112333425},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.462754080467114,\n", + " 'value_error': 40.685263608734644},\n", + " 'HadISST': {'value': 60.03299695126412,\n", + " 'value_error': 35.43240243596692},\n", + " 'Tropflux': {'value': 35.708801612674975,\n", + " 'value_error': 40.46047879395292}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 1.6673950962698272,\n", + " 'value_error': 0.46928729044917866},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 18.53190151208758,\n", + " 'value_error': 48.85571815597747},\n", + " 'HadISST': {'value': 0.20483948275046993,\n", + " 'value_error': 44.5388334777},\n", + " 'Tropflux': {'value': 18.790944295807964,\n", + " 'value_error': 48.70037242597173}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 22.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.08163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10594643285851656,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11201029769330018, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10368044888861061, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 0.37082615674086633,\n", + " 'value_error': 0.051926048192088424},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 5.154968809662722,\n", + " 'value_error': 28.27727699004331},\n", + " 'HadISST': {'value': 4.8917595533795595,\n", + " 'value_error': 21.057616333384196},\n", + " 'Tropflux': {'value': 6.698056936721838,\n", + " 'value_error': 27.817217777227704}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1112762781583933,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1310091487007761, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11105565220259263, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1717976762824132,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2505784433800873, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5947312989394321,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.28294683515304103, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15819741813681992,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17227648746271207, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1642547281404068, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.271908750618941,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.224010859934307, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r5i1p1',\n", + " 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N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.529047755465619,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5656653701562622, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8098290161499953,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8410447638898261, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4136856615185079,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26677998462855274, 'value_error': None},\n", + " 'Tropflux': {'value': 0.46287795029891776, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.884498621793761,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.967871435992923, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 1.0403448352999638,\n", + " 'value_error': 0.14567741533919393},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 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'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 1.6787629859427564,\n", + " 'value_error': 0.47248677577492354},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 17.976472053559263,\n", + " 'value_error': 49.18880442637094},\n", + " 'HadISST': {'value': 0.888011433106951,\n", + " 'value_error': 44.84248829008859},\n", + " 'Tropflux': {'value': 18.23728092726785,\n", + " 'value_error': 49.03239958738509}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 16.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 50.37593984962406,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 66.3265306122449, 'value_error': None},\n", + " 'Tropflux': {'value': 48.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11975850515089737,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13810949979494608, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11927162832416574, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 0.16160706392280233,\n", + " 'value_error': 0.022629515304934157},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 58.66627329353151,\n", + " 'value_error': 12.323315459340972},\n", + " 'HadISST': {'value': 58.55156597223823,\n", + " 'value_error': 9.1769673929154},\n", + " 'Tropflux': {'value': 59.338755363782084,\n", + " 'value_error': 12.122820382976329}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0782185121296504,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08988408373568677, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07669455111321322, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2364964711516766,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3151386471853532, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6483134697806969,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3286039493051741, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15692101232717906,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17808060121794736, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1593134393820109, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3340413463344616,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.3073300901350176, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r6i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5232276001617249,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5409261358324, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.822951205469627,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8378355583486604, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4527496331083414,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29216523928788957, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5020249905198529, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.008727793084806,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.121477343786555, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 1.068322008540984,\n", + " 'value_error': 0.14959500318887411},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.836532642887622,\n", + " 'value_error': 35.4301486109017},\n", + " 'HadISST': {'value': 39.36232339942333,\n", + " 'value_error': 30.855773629006777},\n", + " 'Tropflux': {'value': 18.179964499779988,\n", + " 'value_error': 35.23439814287523}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 1.8011727483294266,\n", + " 'value_error': 0.5069389256243987},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 11.99559169694257,\n", + " 'value_error': 52.77548694935681},\n", + " 'HadISST': {'value': 8.24442660940117,\n", + " 'value_error': 48.11225202825809},\n", + " 'Tropflux': {'value': 12.275417878351135,\n", + " 'value_error': 52.60767759446448}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15663528330719068,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1730517351162373, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15651187285268492, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 0.00400989646363735,\n", + " 'value_error': 0.0005614978157045727},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 98.97440148638307,\n", + " 'value_error': 0.30577388067828576},\n", + " 'HadISST': {'value': 98.97155529593297,\n", + " 'value_error': 0.22770470672832158},\n", + " 'Tropflux': {'value': 98.99108753592758,\n", + " 'value_error': 0.30079907030690584}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16699110871798495,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16475804341918532, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16924418566438107, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2193122455771268,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2893008384843003, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6599712788529258,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3399090446604427, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15269875720515236,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17087869843818096, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1569156411223494, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.384812043424532,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.3326857407145294, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,\n", + " 'name': 'CESM1-WACCM_r7i1p1',\n", + " 'nyears': 51,\n", + " 'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CESM1-WACCM_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5506991388326379,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6002685552516014, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8543507137915616,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8953633874639568, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4178805768796968,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2703830296284637, 'value_error': None},\n", + " 'Tropflux': {'value': 0.46668882478710805, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.239439265596192,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.299831085389021, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 1.0997485173321233,\n", + " 'value_error': 0.15399559462595047},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.332311357500675,\n", + " 'value_error': 36.47238668883152},\n", + " 'HadISST': {'value': 43.461903157634005,\n", + " 'value_error': 31.76344868714182},\n", + " 'Tropflux': {'value': 21.656429145829073,\n", + " 'value_error': 36.270877887872544}}},\n", + " 'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 1.4897500318802577,\n", + " 'value_error': 0.4192891999452828},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.21155136469755,\n", + " 'value_error': 43.65060677173911},\n", + " 'HadISST': {'value': 10.4710316421452,\n", + " 'value_error': 39.793645034551346},\n", + " 'Tropflux': {'value': 27.442995607378318,\n", + " 'value_error': 43.51181164948751}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 27.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.293233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 43.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 14.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10954538435674577,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11968488906005038, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10777047549498144, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 0.5836331105912387,\n", + " 'value_error': 0.08172498211402283},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.273991522721836,\n", + " 'value_error': 44.504830171086695},\n", + " 'HadISST': {'value': 49.688249347280575,\n", + " 'value_error': 33.14200441064922},\n", + " 'Tropflux': {'value': 46.845367470343405,\n", + " 'value_error': 43.78075560258381}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10057889472045672,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06976768208225444, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09481267218414956, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2301786685568041,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3108884104806142, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6348017222104968,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3388197739161915, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15275236405543766,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17137882208223876, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15680231220121144, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2470734351422554,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.20939340132962, 'value_error': None}}}}}},\n", + " 'CMCC-CESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.266289439111172,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8569851774558324, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5721691923087384,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7776093593350174, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6522912894845201,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5955537502759106, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6909960981570102, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.293877908723839,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.932758778474601, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 1.5833757257633487,\n", + " 'value_error': 0.12677151587313748},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 76.12936887596994,\n", + " 'value_error': 41.9501337592839},\n", + " 'HadISST': {'value': 106.55094455836362,\n", + " 'value_error': 33.346188192401634},\n", + " 'Tropflux': {'value': 75.15625959637556,\n", + " 'value_error': 41.71836057629969}}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 1.0759924078330605,\n", + " 'value_error': 0.17257412885968462},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 47.427543927834414,\n", + " 'value_error': 25.16265195511611},\n", + " 'HadISST': {'value': 35.336473789100054,\n", + " 'value_error': 20.913122887678647},\n", + " 'Tropflux': {'value': 47.5947077087587,\n", + " 'value_error': 25.08264268118899}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 33.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7518796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 32.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 3.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2365344825960915,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2429666690140071, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24249067110477376, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 0.5119893976275091,\n", + " 'value_error': 0.04099195850493584},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 30.94990605270766,\n", + " 'value_error': 31.189381474166115},\n", + " 'HadISST': {'value': 31.31331177834144,\n", + " 'value_error': 21.19960485337213},\n", + " 'Tropflux': {'value': 28.819407040440726,\n", + " 'value_error': 30.681943565832054}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22559615432647281,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2277181541447665, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22694233072047126, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3149405335838724,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9049764863882265, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7549334880825154,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.557180794868068, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19416106023563937,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20401580968219135, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1987083364846413, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.3402398198644625,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.1478877166514523, 'value_error': None}}}}}},\n", + " 'CMCC-CM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.794313839575357,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5226887738548718, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6570349111924554,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6917847320856454, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28805626601671697,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.35305223611814474, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29655926504915847, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.537743136545268,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 12.21983656023157, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 0.6896586577646219,\n", + " 'value_error': 0.05521688381177166},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 23.28469979961902,\n", + " 'value_error': 18.271893695683797},\n", + " 'HadISST': {'value': 10.034210537451168,\n", + " 'value_error': 14.524340001013375},\n", + " 'Tropflux': {'value': 23.708549444844458,\n", + " 'value_error': 18.170942051875016}}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 1.1994837192542982,\n", + " 'value_error': 0.19238040754261618},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.39381962111569,\n", + " 'value_error': 28.050561633801845},\n", + " 'HadISST': {'value': 27.915061152009645,\n", + " 'value_error': 23.31331544713547},\n", + " 'Tropflux': {'value': 41.580168736786014,\n", + " 'value_error': 27.961369720581743}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22416465729429,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24286757066299722, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22521550219966296, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 0.09929962732097122,\n", + " 'value_error': 0.007950332998220157},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 74.60244893936179,\n", + " 'value_error': 6.049136898357312},\n", + " 'HadISST': {'value': 74.53196690733347,\n", + " 'value_error': 4.111633699929177},\n", + " 'Tropflux': {'value': 75.01565624190776,\n", + " 'value_error': 5.950720026016601}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1419127308267677,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10191081369894583, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13767509535413938, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1395376159007269,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9009787259713443, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5466052774835333,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.337118944792206, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2522599070399602,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2555574281572348, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25975884684664274, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.184292229174057,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.894092376800855, 'value_error': None}}}}}},\n", + " 'CMCC-CMS': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,\n", + " 'name': 'CMCC-CMS_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CMCC-CMS_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8643469097126824,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5727663588546843, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5860238552380699,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0386825785330056, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2279858319558705,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3141430777245235, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24955663334575562, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.474226246261977,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.212789267032081, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 0.9067222295714813,\n", + " 'value_error': 0.07259587831765683},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8607189321697079,\n", + " 'value_error': 24.02280027041718},\n", + " 'HadISST': {'value': 18.281674982584896,\n", + " 'value_error': 19.09573932046224},\n", + " 'Tropflux': {'value': 0.30346660017178917,\n", + " 'value_error': 23.890075046826283}}},\n", + " 'EnsoDuration': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 1.231204859833289,\n", + " 'value_error': 0.1974680347061568},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.84394040495424,\n", + " 'value_error': 28.792377295508903},\n", + " 'HadISST': {'value': 26.00872725008173,\n", + " 'value_error': 23.929851499096802},\n", + " 'Tropflux': {'value': 40.03521764628401,\n", + " 'value_error': 28.700826643130988}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 51.8796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 67.3469387755102, 'value_error': None},\n", + " 'Tropflux': {'value': 50.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13747009167323185,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16139402721507679, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13893028817510408, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 0.18759310240447552,\n", + " 'value_error': 0.015019468577298658},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 52.019906564795335,\n", + " 'value_error': 11.427800770734413},\n", + " 'HadISST': {'value': 51.88675457411136,\n", + " 'value_error': 7.767542965970545},\n", + " 'Tropflux': {'value': 52.8005221815109,\n", + " 'value_error': 11.241875335670466}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14683978156855543,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15034197083660594, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14871852509890268, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1562622358501051,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9398978306235167, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1132593148456569,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7875028986036792, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1945714640588451,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2008177642036725, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19948056790143612, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.055333690012454,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.812870464891856, 'value_error': None}}}}}},\n", + " 'CNRM-CM5': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6327526926915785,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8251856205463148, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.046767574816917,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5259991472697271, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.948739957544449,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8063245203886426, 'value_error': None},\n", + " 'Tropflux': {'value': 0.992217835256582, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.02148868583172,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.745625504957925, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 0.8070603664101351,\n", + " 'value_error': 0.06461654324125604},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 10.225330180493467,\n", + " 'value_error': 21.38234770929033},\n", + " 'HadISST': {'value': 5.280811297815292,\n", + " 'value_error': 16.996841888533094},\n", + " 'Tropflux': {'value': 10.721332436286499,\n", + " 'value_error': 21.264210903894305}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 1.870275195396393,\n", + " 'value_error': 0.2999659758040646},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 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{'ERA-Interim': {'value': 61.946763728399915,\n", + " 'value_error': 9.06344218317955},\n", + " 'HadISST': {'value': 61.84116026248808,\n", + " 'value_error': 6.160474617104538},\n", + " 'Tropflux': {'value': 62.56587362113686,\n", + " 'value_error': 8.915983851966775}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21651756318496246,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21868079561600035, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21845515534715126, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9125054299883032,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9725046255209019, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3105167981832129,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3279126271711382, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21056196101117403,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2169178976876737, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20633625518804052, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3825457014095948,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1723026432316934, 'value_error': None}}}}},\n", + " 'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6290348255113536,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8393549854238036, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0737834031253808,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6088406630868743, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9389352952216332,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8026619793401767, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9816133743163603, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.279794895939528,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.985043429621603, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 0.8763227560779431,\n", + " 'value_error': 0.07016197253407336},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 2.520815844105439,\n", + " 'value_error': 23.21739321603599},\n", + " 'HadISST': {'value': 14.316071707253567,\n", + " 'value_error': 18.455520737108156},\n", + " 'Tropflux': {'value': 3.0593853017419463,\n", + " 'value_error': 23.089117841345768}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 2.130428262344567,\n", + " 'value_error': 0.3416908881472397},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 4.091669626712491,\n", + " 'value_error': 49.82119250142169},\n", + " 'HadISST': {'value': 28.031576040575068,\n", + " 'value_error': 41.40726991143243},\n", + " 'Tropflux': {'value': 3.7606910429149676,\n", + " 'value_error': 49.66277686839015}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 31.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.7669172932330826,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 36.734693877551024, 'value_error': None},\n", + " 'Tropflux': {'value': 3.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08694435742833558,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09552839161211654, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08418179539380312, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 0.20055649169535805,\n", + " 'value_error': 0.016057370374401487},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 48.70430155884797,\n", + " 'value_error': 12.217504806921532},\n", + " 'HadISST': {'value': 48.56194826455546,\n", + " 'value_error': 8.304309414261505},\n", + " 'Tropflux': {'value': 49.538860652143,\n", + " 'value_error': 12.018731224655381}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19777785174897639,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20941185529550774, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20107738356306637, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9045568844723757,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.956241146263163, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3134097862257685,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.31137109274298225, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20570278702396436,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2122795542516941, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20186147599743406, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5095376902792084,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.282804953403897, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.616337255429204,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8137278728548867, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0682358974372694,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5808347212176553, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9529456369841307,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8123101960998825, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9960416114199253, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.003271630217473,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.73510748662379, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 0.9005362468330464,\n", + " 'value_error': 0.07210060331996898},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17261110162120813,\n", + " 'value_error': 23.858908151138376},\n", + " 'HadISST': {'value': 17.47471517080083,\n", + " 'value_error': 18.965461369882096},\n", + " 'Tropflux': {'value': 0.380839456035829,\n", + " 'value_error': 23.727088426404034}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 2.145982047866135,\n", + " 'value_error': 0.34418549774421686},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 4.851619882992906,\n", + " 'value_error': 50.18492600810313},\n", + " 'HadISST': {'value': 28.96630625840079,\n", + " 'value_error': 41.70957523032982},\n", + " 'Tropflux': {'value': 4.518224897763047,\n", + " 'value_error': 50.02535381757423}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 31.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.015037593984962,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 36.224489795918366, 'value_error': None},\n", + " 'Tropflux': {'value': 2.34375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0763946982350786,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09624659623227642, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07467484860486504, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 0.1835117829497327,\n", + " 'value_error': 0.014692701502610282},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 53.063772710551916,\n", + " 'value_error': 11.179174861717991},\n", + " 'HadISST': {'value': 52.93351760575048,\n", + " 'value_error': 7.598550482685031},\n", + " 'Tropflux': {'value': 53.82740507115442,\n", + " 'value_error': 10.997294463947696}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17241890994994039,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18195685090591932, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17440131160560168, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9197798194302927,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9746418775812541, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31243768002704225,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2936118425426857, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2029331975673081,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2093319407075398, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19929762797179693, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.40943823322712,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1787161912621045, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6085914444456495,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8029320619812175, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9955265188110903,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5222866116961783, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9284319668399101,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.788856437362303, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9715830490165797, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.823501954002632,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.546110851607816, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 0.8154330121146953,\n", + " 'value_error': 0.0652868913908237},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.29398534567522,\n", + " 'value_error': 21.604173522020954},\n", + " 'HadISST': {'value': 6.373020715068862,\n", + " 'value_error': 17.173171369141986},\n", + " 'Tropflux': {'value': 9.795133252685012,\n", + " 'value_error': 21.484811135915702}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 2.0349723154145565,\n", + " 'value_error': 0.3263810897081426},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5722606578063486,\n", + " 'value_error': 47.58890465983447},\n", + " 'HadISST': {'value': 22.29499455416305,\n", + " 'value_error': 39.55197619934449},\n", + " 'Tropflux': {'value': 0.8884094185638893,\n", + " 'value_error': 47.43758699603609}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 28.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.285714285714285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 41.83673469387755, 'value_error': None},\n", + " 'Tropflux': {'value': 10.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08029645936505095,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09699340221980993, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0786285658546775, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': -0.02540053968996388,\n", + " 'value_error': -0.0020336707631033782},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 106.49661555786507,\n", + " 'value_error': -1.547350639898122},\n", + " 'HadISST': {'value': 106.51464464518658,\n", + " 'value_error': -1.051743272389782},\n", + " 'Tropflux': {'value': 106.39091839895653,\n", + " 'value_error': -1.5221759062209066}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1729594485363155,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17956946406933033, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1758175780214782, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8976600446210503,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9623606329443285, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3001256540667008,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.34302008210932844, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2021306389229976,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2095025037908731, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19813072578788266, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4169841202309725,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.19117354759193, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.613071787092603,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8076246599265753, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9761826204073191,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5013226161227439, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9281951962636964,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7889755515060173, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9715521793899784, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.760185587540803,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.479800241121154, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 0.8010689432025278,\n", + " 'value_error': 0.06413684547280649},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 10.891795865835446,\n", + " 'value_error': 21.223610272004915},\n", + " 'HadISST': {'value': 4.499231725358738,\n", + " 'value_error': 16.870661397970835},\n", + " 'Tropflux': {'value': 11.384115919466078,\n", + " 'value_error': 21.10635048600791}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 1.8962220034260986,\n", + " 'value_error': 0.3041274808108086},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 7.351532173952771,\n", + " 'value_error': 44.344155176647575},\n", + " 'HadISST': {'value': 13.95656728393253,\n", + " 'value_error': 36.855207798197384},\n", + " 'Tropflux': {'value': 7.64612499566505,\n", + " 'value_error': 44.20315478984719}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 33.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7518796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 32.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 3.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08393892589562683,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1039044054161176, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0827280094185649, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': -0.08755544572372777,\n", + " 'value_error': -0.0070100459396610195},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 122.39377894357604,\n", + " 'value_error': -5.3337045834780135},\n", + " 'HadISST': {'value': 122.4559250552609,\n", + " 'value_error': -3.625350174642307},\n", + " 'Tropflux': {'value': 122.02944172976385,\n", + " 'value_error': -5.246927489172579}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25553014559140164,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25342711405931906, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2567353727106157, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9233304219448546,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9872408397041962, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31625131914790744,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3644611528557881, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2148529891000889,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22155713275462494, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21086168298829927, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2449332018773096,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0431614759152157, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6298136922511874,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8356127529967345, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0360940403934027,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5726206643173734, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.925740748237872,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7896216622978829, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9682318853075853, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.213777274540618,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.936288290444702, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 0.884577067263232,\n", + " 'value_error': 0.07082284633958986},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 1.60263414161034,\n", + " 'value_error': 23.436083860763848},\n", + " 'HadISST': {'value': 15.392844417772512,\n", + " 'value_error': 18.62935807066371},\n", + " 'Tropflux': {'value': 2.146276524567331,\n", + " 'value_error': 23.306600227070014}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 2.0697188347365647,\n", + " 'value_error': 0.3319539452963877},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1254370651689822,\n", + " 'value_error': 48.40147040471013},\n", + " 'HadISST': {'value': 24.38314354718533,\n", + " 'value_error': 40.22731389058681},\n", + " 'Tropflux': {'value': 0.8038901626573346,\n", + " 'value_error': 48.247569038868654}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 38.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.285714285714285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 22.448979591836736, 'value_error': None},\n", + " 'Tropflux': {'value': 18.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.06972954091274113,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09009986805581008, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06796851653033027, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': -0.08385880995902635,\n", + " 'value_error': -0.006714078209515272},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 121.4483021263961,\n", + " 'value_error': -5.108512844018985},\n", + " 'HadISST': {'value': 121.50782439741414,\n", + " 'value_error': -3.4722860333502084},\n", + " 'Tropflux': {'value': 121.09934741636602,\n", + " 'value_error': -5.025399523082698}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15994643988601334,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1697542380374311, 'value_error': None},\n", + " 'Tropflux': {'value': 0.162754779610357, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9141952020796642,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9701840401763726, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30133640288333075,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2905302760656057, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19464147470573123,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20132336210401264, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19120996436718224, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4898943709667694,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.27176732862808, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6142804219657014,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8113129174936315, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0846143538189332,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5694715454598199, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9495285792059064,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8096341590840441, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9926616337446056, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.073752788487056,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.803118750241032, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 0.8351427654098207,\n", + " 'value_error': 0.06686493459437479},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 7.1015389464493515,\n", + " 'value_error': 22.126365932602702},\n", + " 'HadISST': {'value': 8.94415281838426,\n", + " 'value_error': 17.588262450753835},\n", + " 'Tropflux': {'value': 7.614800051557312,\n", + " 'value_error': 22.004118449686437}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 1.910319118959289,\n", + " 'value_error': 0.3063884609207651},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 6.662754091767755,\n", + " 'value_error': 44.67382368466903},\n", + " 'HadISST': {'value': 14.803756532799397,\n", + " 'value_error': 37.12920109718464},\n", + " 'Tropflux': {'value': 6.959537009911476,\n", + " 'value_error': 44.53177505629178}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 27.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.796992481203006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 44.89795918367347, 'value_error': None},\n", + " 'Tropflux': {'value': 15.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08248648521413164,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09051977637845364, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07977413467836339, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 0.3156882108193165,\n", + " 'value_error': 0.02527528518826414},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 19.257426539889526,\n", + " 'value_error': 19.231101424690138},\n", + " 'HadISST': {'value': 19.03335372928361,\n", + " 'value_error': 13.071492021611276},\n", + " 'Tropflux': {'value': 20.57107370612282,\n", + " 'value_error': 18.918219622593877}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17151699594358305,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1742597713036354, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17294435544054346, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9218142807317379,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9748424320523957, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.298802391816,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.31200313338594254, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20234029210602528,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20973990006097046, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1985077644023716, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4162640390840027,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.193306687517343, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6286879553043083,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8320878878341564, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0174549561402786,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5624629356137915, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9270870073761847,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.788230133175401, 'value_error': None},\n", + " 'Tropflux': {'value': 0.969919462798235, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.310529491386033,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.016023136568297, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 0.8543060147213272,\n", + " 'value_error': 0.06839922246095385},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 4.969883804886973,\n", + " 'value_error': 22.634079205453745},\n", + " 'HadISST': {'value': 11.44399362160917,\n", + " 'value_error': 17.991844056510317},\n", + " 'Tropflux': {'value': 5.49492223828713,\n", + " 'value_error': 22.509026622511637}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 2.044532041864374,\n", + " 'value_error': 0.3279143360881394},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10517715871633915,\n", + " 'value_error': 47.81246392260595},\n", + " 'HadISST': {'value': 22.869501973877778,\n", + " 'value_error': 39.73778023714458},\n", + " 'Tropflux': {'value': 0.42281109725610216,\n", + " 'value_error': 47.6604354110668}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 33.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7518796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 32.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 3.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0678180470672231,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09609661240550325, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06719078483753517, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': -0.026239413714572456,\n", + " 'value_error': -0.0021008344375211853},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 106.71117170926505,\n", + " 'value_error': -1.5984531863249156},\n", + " 'HadISST': {'value': 106.7297962222439,\n", + " 'value_error': -1.0864779718305655},\n", + " 'Tropflux': {'value': 106.60198381346015,\n", + " 'value_error': -1.5724470360551377}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2451488305613013,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2463469447633226, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24671262701864552, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9205999530675656,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9867261892960254, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2809559730449365,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.32191978697368984, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19872399870334453,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20556325575385948, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19497069944820736, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4476001410905264,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.2229732111987044, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5791593840744211,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7802071390271414, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9730322684966817,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5225638385489614, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9360606133488433,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7956340906512755, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9793962087164375, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.986529906939293,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.695506711614549, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 0.8230192970802105,\n", + " 'value_error': 0.06589428029370732},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.450112624028385,\n", + " 'value_error': 21.8051654052873},\n", + " 'HadISST': {'value': 7.362649581938498,\n", + " 'value_error': 17.33293994587654},\n", + " 'Tropflux': {'value': 8.955922901552942,\n", + " 'value_error': 21.684692545284502}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 2.1128914982028633,\n", + " 'value_error': 0.33887823652187443},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2348320172869998,\n", + " 'value_error': 49.411085990163556},\n", + " 'HadISST': {'value': 26.97767547447797,\n", + " 'value_error': 41.06642317229392},\n", + " 'Tropflux': {'value': 2.9065779060580224,\n", + " 'value_error': 49.253974366115536}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 35.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.7669172932330826,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 27.55102040816326, 'value_error': None},\n", + " 'Tropflux': {'value': 10.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0774587997093171,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09885745000602252, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07602267935898231, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 0.06824905862819845,\n", + " 'value_error': 0.005464297878534285},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 82.5441545138197,\n", + " 'value_error': 4.1575976664194085},\n", + " 'HadISST': {'value': 82.49571191170814,\n", + " 'value_error': 2.8259434301510167},\n", + " 'Tropflux': {'value': 82.82815366042178,\n", + " 'value_error': 4.089955329065271}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20450536208574668,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2134818198537224, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20717291432001853, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.890662319729005,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9520207987999401, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31871218047616756,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3547534728451012, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22180580199790714,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22862159759000478, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21756443122945984, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4027309712696185,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1884989606386216, 'value_error': None}}}}},\n", + " 'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6390593484462643,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8437662380381334, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0412655054267332,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5692339698621006, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9005652203779269,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7704715060992173, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9423865280342631, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.327180627785003,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.0476866379755, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 0.8223377357455437,\n", + " 'value_error': 0.06583971171459475},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.525927205350992,\n", + " 'value_error': 21.787108042976378},\n", + " 'HadISST': {'value': 7.273740086131989,\n", + " 'value_error': 17.31858613701067},\n", + " 'Tropflux': {'value': 9.03131860967408,\n", + " 'value_error': 21.666734949338167}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 2.183957892998788,\n", + " 'value_error': 0.35027629201355215},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 6.70710086548467,\n", + " 'value_error': 51.07301124626831},\n", + " 'HadISST': {'value': 31.248526875609254,\n", + " 'value_error': 42.44767850154346},\n", + " 'Tropflux': {'value': 6.367806037644439,\n", + " 'value_error': 50.910615225595514}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 16.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 51.127819548872175,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 66.83673469387756, 'value_error': None},\n", + " 'Tropflux': {'value': 49.21875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09400374740933934,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09891452397583499, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09119203983763835, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 0.1633490049865521,\n", + " 'value_error': 0.013078387297197281},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 58.2207425467926,\n", + " 'value_error': 9.950898306799816},\n", + " 'HadISST': {'value': 58.1047988105282,\n", + " 'value_error': 6.763683730470197},\n", + " 'Tropflux': {'value': 58.900473211902636,\n", + " 'value_error': 9.789001443694982}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17655106589947478,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18881867497618, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17988215000096422, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9286384278053891,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9812327328921392, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.308133663190084,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3050293527764556, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1922932334631617,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1997049902271164, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18862155861423666, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6679600756932946,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.41250279982442, 'value_error': None}}}}}},\n", + " 'CNRM-CM5-2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CNRM-CM5-2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CNRM-CM5-2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.546830743500996,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6856487222018874, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1899388877905694,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.46131160529063586, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.123183134209089,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9518632076280573, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1698740404005803, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.983768153163314,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.777150791809402, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 0.8082962797594796,\n", + " 'value_error': 0.06471549550270284},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 10.08785135303419,\n", + " 'value_error': 21.415092135945528},\n", + " 'HadISST': {'value': 5.442035867287785,\n", + " 'value_error': 17.02287045425263},\n", + " 'Tropflux': {'value': 10.584613175068796,\n", + " 'value_error': 21.296774418611673}}},\n", + " 'EnsoDuration': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 2.006342202206911,\n", + " 'value_error': 0.32178922009085237},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9711138076917722,\n", + " 'value_error': 46.91937430921574},\n", + " 'HadISST': {'value': 20.574421005180486,\n", + " 'value_error': 38.99551774160736},\n", + " 'Tropflux': {'value': 2.282814657909016,\n", + " 'value_error': 46.77018554015074}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 29.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.278195488721805,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 39.795918367346935, 'value_error': None},\n", + " 'Tropflux': {'value': 7.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11677306822396072,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1318314773401999, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11583316546012414, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 0.06814084451954626,\n", + " 'value_error': 0.0054556338158172155},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 82.57183209352593,\n", + " 'value_error': 4.151005476949704},\n", + " 'HadISST': {'value': 82.52346630086954,\n", + " 'value_error': 2.821462680444836},\n", + " 'Tropflux': {'value': 82.85538093773374,\n", + " 'value_error': 4.083470391701179}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18613035958377797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19011475399837208, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18737082430262175, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9205097825840353,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9691612679670483, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3572071094349826,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.2915896240405753, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20941387154419366,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2180980256716583, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20495642063495542, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2632346539414,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0776427816291942, 'value_error': None}}}}}},\n", + " 'CSIRO-Mk3-6-0': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r10i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.103062955641847,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1568624198108393, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.999960758987316,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.885963741847579, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.736449971602806,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.5394959913847375, 'value_error': None},\n", + " 'Tropflux': {'value': 2.7840662390760706, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.647767632540104,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.824095304559902, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.7150174688994879,\n", + " 'value_error': 0.057247213616630654},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.46387128244847,\n", + " 'value_error': 18.943752877162144},\n", + " 'HadISST': {'value': 6.726160333347822,\n", + " 'value_error': 15.058401294665671},\n", + " 'Tropflux': {'value': 20.903305917410265,\n", + " 'value_error': 18.83908923229272}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.8887573971141307,\n", + " 'value_error': 0.1425442525969646},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.57575380787995,\n", + " 'value_error': 20.784062130285466},\n", + " 'HadISST': {'value': 46.58866844687136,\n", + " 'value_error': 17.27399982367249},\n", + " 'Tropflux': {'value': 56.7138291750893,\n", + " 'value_error': 20.7179753869936}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 43.75,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 31.57894736842105,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.714285714285714, 'value_error': None},\n", + " 'Tropflux': {'value': 36.71875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6061410050847337,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6135012813602986, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6055744065107667, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.3201032241932767,\n", + " 'value_error': 0.02562876915856265},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 18.128212557684837,\n", + " 'value_error': 19.500055307274586},\n", + " 'HadISST': {'value': 17.901006008086902,\n", + " 'value_error': 13.254301547324157},\n", + " 'Tropflux': {'value': 19.46023155285825,\n", + " 'value_error': 19.182797740441533}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15605316254467175,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11608296023415784, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15252657059887686, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8319476240372634,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.791325697453209, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9435473006305822,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6667267515125673, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.186962241839423,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20427004683234695, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1867189599012703, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4871952450868946,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.665453995412361, 'value_error': None}}}}},\n", + " 'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0999288319356701,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1778570558781354, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9834835477576234,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8786756716011133, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7469656428573876,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.5493675503111612, 'value_error': None},\n", + " 'Tropflux': {'value': 2.794557533567746, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.18271509008928,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.333742722076048, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 0.7753500154422521,\n", + " 'value_error': 0.06207768326275682},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 13.752682540348632,\n", + " 'value_error': 20.542210120332904},\n", + " 'HadISST': {'value': 1.144204402736249,\n", + " 'value_error': 16.329015981001042},\n", + " 'Tropflux': {'value': 14.229196284170719,\n", + " 'value_error': 20.428715048959816}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 0.8840003945137872,\n", + " 'value_error': 0.14178129593132155},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.80817859863231,\n", + " 'value_error': 20.672817106704787},\n", + " 'HadISST': {'value': 46.87454831005006,\n", + " 'value_error': 17.181542126728015},\n", + " 'Tropflux': {'value': 56.94551492852621,\n", + " 'value_error': 20.607084087399585}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 46.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 38.34586466165413,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5679695008683221,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5768122060615655, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5676603507985688, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': -0.1484329865830473,\n", + " 'value_error': -0.011884150052659316},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 137.96423468580142,\n", + " 'value_error': -9.042243967045184},\n", + " 'HadISST': {'value': 138.06959115889862,\n", + " 'value_error': -6.146066065719282},\n", + " 'Tropflux': {'value': 137.34657281083213,\n", + " 'value_error': -8.89513052175022}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1758680575810676,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13820258864686386, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17109046685130652, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8340454022462553,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8062524167561979, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9245837325645694,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6532167273351532, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19083134212122738,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20906964051973037, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1897895761131959, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5335940977014926,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.710900663913964, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0920747080145772,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1354501961830117, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.065497485056934,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.955845597485687, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.750864452057514,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.5531521808473694, 'value_error': None},\n", + " 'Tropflux': {'value': 2.7984830917478956, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.037441184457306,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.21360395967189, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.6589717670781621,\n", + " 'value_error': 0.05275996623595099},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 26.698205893863552,\n", + " 'value_error': 17.458871778013},\n", + " 'HadISST': {'value': 14.037307309565547,\n", + " 'value_error': 13.87806835795341},\n", + " 'Tropflux': {'value': 27.103196024191163,\n", + " 'value_error': 17.36241205498749}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.8011751988417978,\n", + " 'value_error': 0.12849729328718382},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 60.854976633112265,\n", + " 'value_error': 18.73590606845143},\n", + " 'HadISST': {'value': 51.85206411059793,\n", + " 'value_error': 15.571741274347758},\n", + " 'Tropflux': {'value': 60.97944542531395,\n", + " 'value_error': 18.676331813576493}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 49.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 47.368421052631575,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 53.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5677142419565733,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5753864483556388, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5672755553432187, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.2543244758591833,\n", + " 'value_error': 0.020362254393388735},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 34.95223460683092,\n", + " 'value_error': 15.49294405811192},\n", + " 'HadISST': {'value': 34.77171725408043,\n", + " 'value_error': 10.530644614399293},\n", + " 'Tropflux': {'value': 36.010533952723826,\n", + " 'value_error': 15.240880478932045}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22517959003780477,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18482107077502874, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22252507450684342, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8379886429863843,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.808806831534962, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9184314945226436,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6414188147010228, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1815207430654919,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1986021038583455, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1816540658364887, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6430869727834647,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8467097450033694, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1231114831605487,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1282689916350699, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.115728189734087,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.997395840968898, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.812199255962905,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.6143379012743466, 'value_error': None},\n", + " 'Tropflux': {'value': 2.8597210225179466, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.301464688669377,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.503139757144217, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.7114046008631784,\n", + " 'value_error': 0.05695795267233282},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.865754522612825,\n", + " 'value_error': 18.848033146897393},\n", + " 'HadISST': {'value': 7.19745801293344,\n", + " 'value_error': 14.982313619774096},\n", + " 'Tropflux': {'value': 21.302968765184566,\n", + " 'value_error': 18.743898350557632}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.8465231657324173,\n", + " 'value_error': 0.135770472748986},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 58.639297433181525,\n", + " 'value_error': 19.79639227582049},\n", + " 'HadISST': {'value': 49.126803760900785,\n", + " 'value_error': 16.45313002522178},\n", + " 'Tropflux': {'value': 58.77081138438942,\n", + " 'value_error': 19.73344601025237}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 48.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 44.3609022556391,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.0408163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 50.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5610430632468513,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5686163786896763, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5606345801947923, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.11216285906589919,\n", + " 'value_error': 0.008980215773861308},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 71.31246091160575,\n", + " 'value_error': 6.832739534939732},\n", + " 'HadISST': {'value': 71.23284866694428,\n", + " 'value_error': 4.644252991253292},\n", + " 'Tropflux': {'value': 71.779194913443,\n", + " 'value_error': 6.721573782561056}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1579483421000817,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12302078956545606, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1559117811526407, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8100448583928331,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7886528269824027, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9045386219725519,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6365329130779693, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17836043092158507,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19619329069167812, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17780781438631177, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6080183078774417,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.807210597011223, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1294476846323303,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1680706725916998, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.06372941066421,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9492258536628104, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.78117454432697,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.5829172607987654, 'value_error': None},\n", + " 'Tropflux': {'value': 2.8287950051175654, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.064136212179404,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.23376869490706, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.7531909861129797,\n", + " 'value_error': 0.060303541034452235},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 16.21757813472013,\n", + " 'value_error': 19.95512631627198},\n", + " 'HadISST': {'value': 1.7464351113002115,\n", + " 'value_error': 15.862342689152625},\n", + " 'Tropflux': {'value': 16.68047340712305,\n", + " 'value_error': 19.844874864638623}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.900206174811902,\n", + " 'value_error': 0.14438047636891485},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.016372200524344,\n", + " 'value_error': 21.051797856321567},\n", + " 'HadISST': {'value': 45.90063539816842,\n", + " 'value_error': 17.496519697571408},\n", + " 'Tropflux': {'value': 56.15622622430506,\n", + " 'value_error': 20.98485979810937}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 45.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 35.338345864661655,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 8.16326530612245, 'value_error': None},\n", + " 'Tropflux': {'value': 40.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5944123845508368,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6029447388067828, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5941317099236546, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.24408815333405348,\n", + " 'value_error': 0.019542692679537503},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 37.57034638649356,\n", + " 'value_error': 14.869367535613012},\n", + " 'HadISST': {'value': 37.397094688524795,\n", + " 'value_error': 10.106796007982828},\n", + " 'Tropflux': {'value': 38.58605017253681,\n", + " 'value_error': 14.627449279979396}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15109398382474887,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11789829239436601, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1475139143171975, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8623350901307807,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8351944564297854, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9173955644318912,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6275697867830168, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18253026067329253,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19978434100886405, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1826369016862916, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.582681192064557,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.7818236910571192, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0897602052146858,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1714498302537828, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.930114889775419,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8350996773553296, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7047582602282283,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.507652592254559, 'value_error': None},\n", + " 'Tropflux': {'value': 2.7524414825783503, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.631385482149463,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.787123684829238, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.753872910115726,\n", + " 'value_error': 0.0603581386502498},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 16.14172321142505,\n", + " 'value_error': 19.973193287151602},\n", + " 'HadISST': {'value': 1.6574783055386775,\n", + " 'value_error': 15.876704135875908},\n", + " 'Tropflux': {'value': 16.605037579917575,\n", + " 'value_error': 19.86284201606662}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.768177377153333,\n", + " 'value_error': 0.12320490433471686},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 62.467233855900325,\n", + " 'value_error': 17.96423454328149},\n", + " 'HadISST': {'value': 53.83512225498745,\n", + " 'value_error': 14.930391488816893},\n", + " 'Tropflux': {'value': 62.586576180112495,\n", + " 'value_error': 17.907113959766356}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 47.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 3.061224489795918, 'value_error': None},\n", + " 'Tropflux': {'value': 48.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.516296148776496,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5233151892451225, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5159838657416896, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.2292332901863051,\n", + " 'value_error': 0.018353351774099417},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 41.36972766789798,\n", + " 'value_error': 13.96443865308421},\n", + " 'HadISST': {'value': 41.20701982561531,\n", + " 'value_error': 9.491710558279241},\n", + " 'Tropflux': {'value': 42.32361714409361,\n", + " 'value_error': 13.737243203663402}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17105773509864536,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14266283295783178, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16747751915353226, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8239978719745303,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8018205298047385, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9230071749318304,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6582019688085113, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20127955566349656,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22020581635917352, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20028760974801124, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.302141949040024,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.4571115506194334, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1228162400204404,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1689741864085437, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.0578274836030594,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.941653352264851, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7862897793750494,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.588475806297217, 'value_error': None},\n", + " 'Tropflux': {'value': 2.8338660144864423, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.143423404236495,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.322318525639801, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.7157229450421121,\n", + " 'value_error': 0.05730369691276663},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.385396498645942,\n", + " 'value_error': 18.962443841072684},\n", + " 'HadISST': {'value': 6.634131157169715,\n", + " 'value_error': 15.073258753848867},\n", + " 'Tropflux': {'value': 20.825264704345084,\n", + " 'value_error': 18.857676929209568}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.8817082056214053,\n", + " 'value_error': 0.14141366090118102},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.92017381251727,\n", + " 'value_error': 20.61921305625377},\n", + " 'HadISST': {'value': 47.012301156114624,\n", + " 'value_error': 17.13699085699861},\n", + " 'Tropflux': {'value': 57.05715403305662,\n", + " 'value_error': 20.553650480873316}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 47.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 41.35338345864661,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 4.081632653061225, 'value_error': None},\n", + " 'Tropflux': {'value': 46.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6216017308145901,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6301621441814379, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6213205115322602, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.25815966246589966,\n", + " 'value_error': 0.02066931506880448},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 33.97132107977094,\n", + " 'value_error': 15.726576040836132},\n", + " 'HadISST': {'value': 33.788081544142806,\n", + " 'value_error': 10.689445638362065},\n", + " 'Tropflux': {'value': 35.04557946957244,\n", + " 'value_error': 15.47071136913598}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18277314713763063,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14931791378491585, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1775575746932016, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8437810196977154,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8052612428220073, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9211012474864425,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6260588735059097, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18723863559032183,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20479909676167404, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1873308625757763, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5528955486003353,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.730830946279003, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r7i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0951896643274894,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.162574054201195, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.960608780298882,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8429935844703746, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.717604328683773,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.520275738586108, 'value_error': None},\n", + " 'Tropflux': {'value': 2.765231447380835, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.481948073107331,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.674028068927456, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.7069017600851039,\n", + " 'value_error': 0.05659743688199716},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 21.36663532524664,\n", + " 'value_error': 18.7287343791675},\n", + " 'HadISST': {'value': 7.784852401248203,\n", + " 'value_error': 14.887482952900191},\n", + " 'Tropflux': {'value': 21.80108221697778,\n", + " 'value_error': 18.62525870480532}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 17.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.76923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.76923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.8468259883283481,\n", + " 'value_error': 0.1358190412568224},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 58.62450167114238,\n", + " 'value_error': 19.803473942503338},\n", + " 'HadISST': {'value': 49.10860514097868,\n", + " 'value_error': 16.459015723035034},\n", + " 'Tropflux': {'value': 58.75606266819345,\n", + " 'value_error': 19.740505159475198}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 33.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7518796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 32.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 3.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6296451351253857,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6380680036425554, 'value_error': None},\n", + " 'Tropflux': {'value': 0.6292870738248049, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.20715563583915061,\n", + " 'value_error': 0.016585724758621065},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 47.01645936980597,\n", + " 'value_error': 12.619511616159206},\n", + " 'HadISST': {'value': 46.86942205905286,\n", + " 'value_error': 8.57755579175901},\n", + " 'Tropflux': {'value': 47.87847893424612,\n", + " 'value_error': 12.414197554897518}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18122186578970922,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15330324787455538, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17827791906864926, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8394614436952469,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7885173778716502, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9436770006042658,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6489470746339031, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18173848912586055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19820396232586354, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18215648416153435, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5193918450344563,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.6981578247438325, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r8i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1006482909155844,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1733162337613159, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.987708103070616,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.887326995953489, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.70226880092786,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.504975162414957, 'value_error': None},\n", + " 'Tropflux': {'value': 2.749900270876695, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.806171090341662,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.965714886130522, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.6943974196728584,\n", + " 'value_error': 0.05559628840961555},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.75757592714156,\n", + " 'value_error': 18.397442984250844},\n", + " 'HadISST': {'value': 9.416040300117533,\n", + " 'value_error': 14.624139210903914},\n", + " 'Tropflux': {'value': 23.18433791535146,\n", + " 'value_error': 18.29579768453146}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.8076125501767865,\n", + " 'value_error': 0.129529754381437},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 60.54045083551733,\n", + " 'value_error': 18.886446936561583},\n", + " 'HadISST': {'value': 51.46520093782041,\n", + " 'value_error': 15.696858439264286},\n", + " 'Tropflux': {'value': 60.66591972026656,\n", + " 'value_error': 18.826394009344185}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 46.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 38.34586466165413,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5830742979865513,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5898223265308155, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5824158195016378, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.1365088005858908,\n", + " 'value_error': 0.010929451107982744},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 65.08557658630365,\n", + " 'value_error': 8.315846140141746},\n", + " 'HadISST': {'value': 64.988683799144,\n", + " 'value_error': 5.652329217829037},\n", + " 'Tropflux': {'value': 65.65361933516066,\n", + " 'value_error': 8.180550877076765}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17553397536255091,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1425626232972626, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17071475784334783, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8246070574430677,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7965647507230589, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9285273409540652,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6647601642207376, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1876589040264123,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20517858463483257, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18731610616688513, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.564408110518108,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.7478596435310143, 'value_error': None}}}}},\n", + " 'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3-6-0_r9i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0997772907135583,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1604990185616746, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.969281522182626,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.862759639079641, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.711977363168962,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.5156652477667727, 'value_error': None},\n", + " 'Tropflux': {'value': 2.7595194037680555, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.15458861726089,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.324793136921414, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 0.7336695904675088,\n", + " 'value_error': 0.058740578512248266},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.38907226505821,\n", + " 'value_error': 19.437924274349424},\n", + " 'HadISST': {'value': 4.2929960090464725,\n", + " 'value_error': 15.451218454784074},\n", + " 'Tropflux': {'value': 18.839970099996254,\n", + " 'value_error': 19.330530347895614}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 15.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 0.820431998701361,\n", + " 'value_error': 0.13158581457803756},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 59.91409893050265,\n", + " 'value_error': 19.186236525346892},\n", + " 'HadISST': {'value': 50.69479518063898,\n", + " 'value_error': 15.946018842623086},\n", + " 'Tropflux': {'value': 60.041559416186175,\n", + " 'value_error': 19.12523036206469}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 47.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 3.061224489795918, 'value_error': None},\n", + " 'Tropflux': {'value': 48.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.606574253950055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6141569822345718, 'value_error': None},\n", + " 'Tropflux': {'value': 0.60599228144321, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': -0.056057944407181974,\n", + " 'value_error': -0.00448822757201513},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 114.33776282798942,\n", + " 'value_error': -3.414939100057751},\n", + " 'HadISST': {'value': 114.37755228077735,\n", + " 'value_error': -2.3211540626260567},\n", + " 'Tropflux': {'value': 114.1044935537766,\n", + " 'value_error': -3.359379500215836}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17625543749214304,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14076549219894513, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1711943118486397, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8190685329046402,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7898975101006535, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9237319436264837,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6560416114867132, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19527066339386528,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21331355599061436, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1939155324800638, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4227206012936864,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5858133322912056, 'value_error': None}}}}}},\n", + " 'CSIRO-Mk3L-1-2': {'r1i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r1i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8614691607209792,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.194556172081318, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9981415986108314,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1418197883716903, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3091007198425894,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4791465225860035, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27824385053115175, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.52555950279072,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.796118071617416, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 0.5710135624017934,\n", + " 'value_error': 0.04586491303865936},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.48237955842031,\n", + " 'value_error': 15.14487565393002},\n", + " 'HadISST': {'value': 25.5114318410649,\n", + " 'value_error': 12.044859480565815},\n", + " 'Tropflux': {'value': 36.83331243386247,\n", + " 'value_error': 15.061200687448304}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 31.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 138.46153846153845,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 138.46153846153845, 'value_error': None},\n", + " 'Tropflux': {'value': 138.46153846153845, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 1.3816004082649933,\n", + " 'value_error': 0.22230534888247985},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.49568840446081,\n", + " 'value_error': 32.344438847078415},\n", + " 'HadISST': {'value': 16.97046042104196,\n", + " 'value_error': 26.895985697353453},\n", + " 'Tropflux': {'value': 32.71033076279977,\n", + " 'value_error': 32.24159376253205}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 8.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.6734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19572447460516995,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21368007361020624, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19636258318155886, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 0.5064701497818019,\n", + " 'value_error': 0.0406806613816887},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 29.538265518301948,\n", + " 'value_error': 30.886562028954728},\n", + " 'HadISST': {'value': 29.897753728694955,\n", + " 'value_error': 21.004567938061477},\n", + " 'Tropflux': {'value': 27.430733294289272,\n", + " 'value_error': 30.384050863589522}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2607667176993489,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22790782032318413, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2574627285418937, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9815665904278619,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8174081889323704, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5619226892766658,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.60727374065074, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.020310532638421985,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.041944795834851036, 'value_error': None},\n", + " 'Tropflux': {'value': 0.021082168697707977, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.750526737836816,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.65489955956396, 'value_error': None}}}}},\n", + " 'r2i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r2i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8706162829620836,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1940705328335477, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.034752408643538,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1029616648015865, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29205633893469485,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.44855395505777135, 'value_error': None},\n", + " 'Tropflux': {'value': 0.26589951893806374, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.496521987614706,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.79023163203276, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 0.5898655445272533,\n", + " 'value_error': 0.047379140681788744},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 34.38534872752416,\n", + " 'value_error': 15.644882911059407},\n", + " 'HadISST': {'value': 23.052195759916902,\n", + " 'value_error': 12.442519870060252},\n", + " 'Tropflux': {'value': 34.747867633020036,\n", + " 'value_error': 15.558445420048809}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 32.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 146.15384615384613,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 146.15384615384613, 'value_error': None},\n", + " 'Tropflux': {'value': 146.15384615384613, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 1.3199821583964506,\n", + " 'value_error': 0.21239071187701156},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 35.506325571484645,\n", + " 'value_error': 30.901903289898115},\n", + " 'HadISST': {'value': 20.673510076818413,\n", + " 'value_error': 25.69644670095037},\n", + " 'Tropflux': {'value': 35.71139505593809,\n", + " 'value_error': 30.803645012129895}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 10.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.92481203007519,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 79.59183673469387, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22527050205531124,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24003137870699043, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22611431570486074, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 0.12433736944243842,\n", + " 'value_error': 0.009987017844105746},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 68.19862496610946,\n", + " 'value_error': 7.582586802905246},\n", + " 'HadISST': {'value': 68.11037135739217,\n", + " 'value_error': 5.156577786111782},\n", + " 'Tropflux': {'value': 68.71601974813524,\n", + " 'value_error': 7.459221355911218}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3281634745011673,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2906834803277875, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3247030288665319, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9778369173433318,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8135273199876261, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5468875177976653,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5731408583207062, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.021562731216689075,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.040889370839449976, 'value_error': None},\n", + " 'Tropflux': {'value': 0.024365213653017942, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.55718019022319,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.451895760860834, 'value_error': None}}}}},\n", + " 'r3i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,\n", + " 'name': 'CSIRO-Mk3L-1-2_r3i2p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8316728374804088,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1575326817445193, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9705009597503037,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0390693416825574, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2626717239976234,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4231823487429132, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23882743478348778, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.970222653761656,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.277526097621173, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 0.5672316225687443,\n", + " 'value_error': 0.04556114032119423},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.90306977432008,\n", + " 'value_error': 15.044568039061376},\n", + " 'HadISST': {'value': 26.004784891808807,\n", + " 'value_error': 11.96508390805327},\n", + " 'Tropflux': {'value': 37.25167834941676,\n", + " 'value_error': 14.961447269028922}}},\n", + " 'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 32.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 146.15384615384613,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 146.15384615384613, 'value_error': None},\n", + " 'Tropflux': {'value': 146.15384615384613, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 1.3495971295821165,\n", + " 'value_error': 0.21715588599116928},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.05935274862581,\n", + " 'value_error': 31.595214915128206},\n", + " 'HadISST': {'value': 18.89374987446161,\n", + " 'value_error': 26.27296928785195},\n", + " 'Tropflux': {'value': 34.26902314895871,\n", + " 'value_error': 31.494752125695687}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 10.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.92481203007519,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 79.59183673469387, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22781759454481432,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24660934533360693, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22900866802339997, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 0.4087390967219699,\n", + " 'value_error': 0.03283071429652342},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 4.541903726591346,\n", + " 'value_error': 24.926534110649897},\n", + " 'HadISST': {'value': 4.832023265643229,\n", + " 'value_error': 16.951419801813124},\n", + " 'Tropflux': {'value': 2.841051627160593,\n", + " 'value_error': 24.520990052599164}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.33366584159753093,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2963451056436713, 'value_error': None},\n", + " 'Tropflux': {'value': 0.33063886140530324, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9502656596250146,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8069896094855523, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.531674386160794,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6131984869072207, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.026182393608958538,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.05100282651686244, 'value_error': None},\n", + " 'Tropflux': {'value': 0.02084600001948026, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.958583803246196,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.84985009083633, 'value_error': None}}}}}},\n", + " 'CanCM4': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r10i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6944065254335208,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1544968765789951, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7464404747419475,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0299918434466926, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9409921626657921,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8882556267459633, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9720148534683192, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.699544556044081,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.395395079427156, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r10i1p1': {'value': 1.0247505099325225,\n", + " 'value_error': 0.15276078667911291},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 13.989786273077796,\n", + " 'value_error': 35.01596178742106},\n", + " 'HadISST': {'value': 33.678432932388205,\n", + " 'value_error': 30.80620080153031},\n", + " 'Tropflux': {'value': 13.359996252769335,\n", + " 'value_error': 34.82249968869942}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r10i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r10i1p1': {'value': 1.4585717879165614,\n", + " 'value_error': 0.4373190149919466},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.734904921150328,\n", + " 'value_error': 44.04673946556393},\n", + " 'HadISST': {'value': 12.344739282719095,\n", + " 'value_error': 40.57170854887354},\n", + " 'Tropflux': {'value': 28.961505381379094,\n", + " 'value_error': 43.906684766648134}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r10i1p1': {'value': 13.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 59.3984962406015,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 72.44897959183673, 'value_error': None},\n", + " 'Tropflux': {'value': 57.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1847980517074336,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21115426257780098, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18509544609404135, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r10i1p1': {'value': -0.36349039087087276,\n", + " 'value_error': -0.05418594821034936},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 192.96878559628456,\n", + " 'value_error': -28.558623981132097},\n", + " 'HadISST': {'value': 193.22678798826269,\n", + " 'value_error': -21.48411742902981},\n", + " 'Tropflux': {'value': 191.4562231832591,\n", + " 'value_error': -28.093987373000324}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18680980348830156,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17172830243174167, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1857703362422313, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.781159647085484,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1323051291630635, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4933312522735083,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7423958818515501, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3612594725049427,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3797708708677685, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3631361819985813, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.747103592808532,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.560965852858891, 'value_error': None}}}}},\n", + " 'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r1i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7201360946498714,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1439012707899594, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7162813334713534,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9657629217892554, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.95454728134312,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8929150806226632, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9872885072663553, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.8336496843905903,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.564487978423361, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r1i1p1': {'value': 0.9838728582016024,\n", + " 'value_error': 0.14666710614371964},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.442694333148228,\n", + " 'value_error': 33.61916297922758},\n", + " 'HadISST': {'value': 28.345954077895957,\n", + " 'value_error': 29.57733081287261},\n", + " 'Tropflux': {'value': 8.838026854243017,\n", + " 'value_error': 33.4334181504346}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r1i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r1i1p1': {'value': 1.4853581705173855,\n", + " 'value_error': 0.4453502922669052},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.42613553545358,\n", + " 'value_error': 44.85564913008496},\n", + " 'HadISST': {'value': 10.734967744561255,\n", + " 'value_error': 41.31679995744357},\n", + " 'Tropflux': {'value': 27.656897468347243,\n", + " 'value_error': 44.713022354305146}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r1i1p1': {'value': 14.75,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 55.639097744360896,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.89795918367348, 'value_error': None},\n", + " 'Tropflux': {'value': 53.90625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20992636420855243,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23074714035046642, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20950133546027822, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r1i1p1': {'value': -0.26220688895284316,\n", + " 'value_error': -0.039087495191153065},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 167.0638252156312,\n", + " 'value_error': -20.601006615129087},\n", + " 'HadISST': {'value': 167.24993743824288,\n", + " 'value_error': -15.497751067000404},\n", + " 'Tropflux': {'value': 165.9727254379556,\n", + " 'value_error': -20.265837040990018}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17198336160402009,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16355238383093687, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17123704332950906, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.834332604221314,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1953447535162443, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5215001404338776,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7687603247312693, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3472190240003725,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3654606392801227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3492312142145828, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.809236061681172,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.630178459702264, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r2i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6565718190408663,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.069814939633353, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7828656846084348,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9796235097611444, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9287854334557132,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8622788922606187, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9626089266458646, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.416465392935939,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.143363620996165, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r2i1p1': {'value': 0.8263391634069046,\n", + " 'value_error': 0.12318336945654303},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.080821904612232,\n", + " 'value_error': 28.236200215417284},\n", + " 'HadISST': {'value': 7.795725266015685,\n", + " 'value_error': 24.841529671215202},\n", + " 'Tropflux': {'value': 8.588672501864416,\n", + " 'value_error': 28.080196088306273}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r2i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r2i1p1': {'value': 1.2514882808871204,\n", + " 'value_error': 0.37522981508732484},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 38.85290249931203,\n", + " 'value_error': 37.79311975530606},\n", + " 'HadISST': {'value': 24.789761837860492,\n", + " 'value_error': 34.8114629702324},\n", + " 'Tropflux': {'value': 39.04733092770293,\n", + " 'value_error': 37.67294958896365}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r2i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 66.9172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 77.55102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21398403022669413,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2355432054206397, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2137738863899347, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r2i1p1': {'value': -0.43094793764222106,\n", + " 'value_error': -0.06424192555542312},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 210.22218860265096,\n", + " 'value_error': -33.85861198994012},\n", + " 'HadISST': {'value': 210.52807178834772,\n", + " 'value_error': -25.47119904854024},\n", + " 'Tropflux': {'value': 208.42892069565963,\n", + " 'value_error': -33.30774684176459}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2421477169746451,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2199314810842114, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23996761960045768, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8127393076115524,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.180845359605651, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5612014977925236,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7774907129324827, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.34036760703016006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.35780493883571723, 'value_error': None},\n", + " 'Tropflux': {'value': 0.34328591547289306, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.7719050263419653,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.56173052166305, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r3i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6751418087849723,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1139097341849589, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6848737267086018,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9468470066596161, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8583656240827348,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.816677377826456, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8881385564660573, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.266359031107471,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.059820933538098, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r3i1p1': {'value': 0.9319376557394643,\n", + " 'value_error': 0.13892506326834927},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 3.6655977898169425,\n", + " 'value_error': 31.844525106682436},\n", + " 'HadISST': {'value': 21.571020655689523,\n", + " 'value_error': 28.016047105073223},\n", + " 'Tropflux': {'value': 3.0928485894549715,\n", + " 'value_error': 31.668585096885376}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r3i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r3i1p1': {'value': 1.3376420290458921,\n", + " 'value_error': 0.40106102380450165},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.64347303906981,\n", + " 'value_error': 40.394837223427224},\n", + " 'HadISST': {'value': 19.612211223491897,\n", + " 'value_error': 37.20792010017841},\n", + " 'Tropflux': {'value': 34.85128612164431,\n", + " 'value_error': 40.26639441849492}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r3i1p1': {'value': 9.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 72.93233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 81.63265306122449, 'value_error': None},\n", + " 'Tropflux': {'value': 71.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2283436324627203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2538094497427658, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2286547368732959, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r3i1p1': {'value': -0.41709223197585865,\n", + " 'value_error': -0.062176438905675424},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 206.67835866454715,\n", + " 'value_error': -32.77000030155177},\n", + " 'HadISST': {'value': 206.97440718805126,\n", + " 'value_error': -24.652256883700588},\n", + " 'Tropflux': {'value': 204.94274735625342,\n", + " 'value_error': -32.23684640034666}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16730610373443375,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16267225979743535, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16690115673332662, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8052461401468826,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1658523199232045, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5112780257212968,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7694240365755568, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3581701893220753,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3764250180888388, 'value_error': None},\n", + " 'Tropflux': {'value': 0.36054676170571615, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5450467256080125,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.389895585804732, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r4i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7092630883257897,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0924569132559945, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7324283677330667,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.950098441498233, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9110823038164013,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8535008097056743, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9434505492635807, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.406697485851918,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.110767675322145, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r4i1p1': {'value': 0.8906386140088649,\n", + " 'value_error': 0.13276856562066788},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9283681506966512,\n", + " 'value_error': 30.43332730479894},\n", + " 'HadISST': {'value': 16.183571587213955,\n", + " 'value_error': 26.77450922816365},\n", + " 'Tropflux': {'value': 1.47573583226148,\n", + " 'value_error': 30.265184118922374}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r4i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r4i1p1': {'value': 1.4827440103400702,\n", + " 'value_error': 0.4445664968011999},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.553862106837375,\n", + " 'value_error': 44.776705307637975},\n", + " 'HadISST': {'value': 10.892069982378851,\n", + " 'value_error': 41.244084342283074},\n", + " 'Tropflux': {'value': 27.784217909635693,\n", + " 'value_error': 44.63432954824253}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r4i1p1': {'value': 12.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 62.40601503759399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 74.48979591836735, 'value_error': None},\n", + " 'Tropflux': {'value': 60.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2517692001028889,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.274814276876957, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25226925452384, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r4i1p1': {'value': -0.3015193885593207,\n", + " 'value_error': -0.044947856623520896},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 177.11865868292628,\n", + " 'value_error': -23.689701453333573},\n", + " 'HadISST': {'value': 177.33267458384105,\n", + " 'value_error': -17.82131828964615},\n", + " 'Tropflux': {'value': 175.86397106149957,\n", + " 'value_error': -23.304280134078017}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19549155212971867,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1608300209455151, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19082997400176435, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8142171719570246,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1685654836525456, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5467814750677404,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7629692894870979, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3452050798776115,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3628454743326972, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3475934354407396, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.909943888049585,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.6877501188032293, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r5i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8010632087520897,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.234917088489838, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5816104505514936,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8986409715304449, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8468532644382257,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8201860154683538, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8740154886291438, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6119870427459473,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.3497838536276667, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r5i1p1': {'value': 0.9888533734610139,\n", + " 'value_error': 0.14740955752258755},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.996710032040145,\n", + " 'value_error': 33.78934833684863},\n", + " 'HadISST': {'value': 28.995660975936737,\n", + " 'value_error': 29.727055796358215},\n", + " 'Tropflux': {'value': 9.388981633646756,\n", + " 'value_error': 33.60266323925319}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r5i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r5i1p1': {'value': 1.5903780513155894,\n", + " 'value_error': 0.47683807449725063},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 22.294916179466963,\n", + " 'value_error': 48.02709627210776},\n", + " 'HadISST': {'value': 4.4236259867355265,\n", + " 'value_error': 44.238038411992584},\n", + " 'Tropflux': {'value': 22.54199376683797,\n", + " 'value_error': 47.87438529758832}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r5i1p1': {'value': 12.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.1578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.0, 'value_error': None},\n", + " 'Tropflux': {'value': 61.71875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2145738992955294,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23915770609689968, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21482731435323968, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r5i1p1': {'value': -0.1924594671356167,\n", + " 'value_error': -0.028690163428575045},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 149.22474812398156,\n", + " 'value_error': -15.121108264695984},\n", + " 'HadISST': {'value': 149.36135421901716,\n", + " 'value_error': -11.37532627028635},\n", + " 'Tropflux': {'value': 148.4238825073616,\n", + " 'value_error': -14.875094294977298}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20535789200957058,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19619269091839636, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2049622215874369, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8724507280516961,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2161145726618048, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5442544125227698,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7828835134827885, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.35873131741232595,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3763252088157234, 'value_error': None},\n", + " 'Tropflux': {'value': 0.36122604181200046, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.55970428547804,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.387314026125043, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r6i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7526087990105615,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1365447244556708, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7636143380416547,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0275735896316902, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9666650868053087,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9028393778419641, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9997054077140392, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.026985979031335,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.735217330610981, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r6i1p1': {'value': 1.0091157713590715,\n", + " 'value_error': 0.1504300974617346},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 12.250630750685836,\n", + " 'value_error': 34.48171915651929},\n", + " 'HadISST': {'value': 31.638885421507613,\n", + " 'value_error': 30.336186987138664},\n", + " 'Tropflux': {'value': 11.630449510493367,\n", + " 'value_error': 34.29120873170078}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r6i1p1': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r6i1p1': {'value': 1.3320826660508156,\n", + " 'value_error': 0.39939417739410843},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.91510076127325,\n", + " 'value_error': 40.22695257389042},\n", + " 'HadISST': {'value': 19.946310248848416,\n", + " 'value_error': 37.05328057059048},\n", + " 'Tropflux': {'value': 35.12205015361069,\n", + " 'value_error': 40.09904358903944}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r6i1p1': {'value': 15.25,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 54.13533834586466,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 68.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 52.34375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20712979872861323,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2282299735271875, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20705742447715011, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r6i1p1': {'value': -0.26002108807334284,\n", + " 'value_error': -0.038761655234363625},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 166.50476985013566,\n", + " 'value_error': -20.42927314711163},\n", + " 'HadISST': {'value': 166.6893306098491,\n", + " 'value_error': -15.368559198518946},\n", + " 'Tropflux': {'value': 165.4227656643539,\n", + " 'value_error': -20.09689760310983}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19982328262209437,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17765743582000249, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19819214603528884, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8435136037194181,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1886098572821415, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4803878601848461,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7472943169892908, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3539980061382118,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3720662489281646, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3559150345177025, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.625304963169237,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.4546619838822594, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r7i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.734196177969259,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1546774591732059, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5884666660230655,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8978584091228663, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8612759348842103,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8313795777103828, 'value_error': None},\n", + " 'Tropflux': {'value': 0.889045238780636, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.3342496059615576,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.116200629058058, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r7i1p1': {'value': 0.9322958270814393,\n", + " 'value_error': 0.13897845629956584},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 3.7054395603992893,\n", + " 'value_error': 31.85676390422622},\n", + " 'HadISST': {'value': 21.617744012499234,\n", + " 'value_error': 28.02681450472347},\n", + " 'Tropflux': {'value': 3.1324702354952465,\n", + " 'value_error': 31.680756275453753}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r7i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r7i1p1': {'value': 1.4573178196754935,\n", + " 'value_error': 0.4369430416181587},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.796173188277045,\n", + " 'value_error': 44.00887145463024},\n", + " 'HadISST': {'value': 12.420098558150444,\n", + " 'value_error': 40.53682810320222},\n", + " 'Tropflux': {'value': 29.022578834796338,\n", + " 'value_error': 43.86893716401103}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r7i1p1': {'value': 22.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.08163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22348885162060703,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24583582295734854, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22352427118825563, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r7i1p1': {'value': -0.05225692679270159,\n", + " 'value_error': -0.007790002706914053},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 113.36558859581318,\n", + " 'value_error': -4.10570942221268},\n", + " 'HadISST': {'value': 113.40268011858396,\n", + " 'value_error': -3.088648228099773},\n", + " 'Tropflux': {'value': 113.14813618091581,\n", + " 'value_error': -4.038911284418227}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09349926410493041,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10172920689432151, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09539926339680817, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.889661069817011,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2506702226016513, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5136740159520888,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7805927027774605, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.34090284683299066,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3582501519315895, 'value_error': None},\n", + " 'Tropflux': {'value': 0.34344790630669775, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5281187877934195,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.3670736301300237, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r8i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7384884750941387,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1434106552553256, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7442368933787922,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9930738853122826, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9089020173536236,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8530191480139407, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9411086287254693, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5187538007523815,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.264019053072433, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r8i1p1': {'value': 0.9101019351761311,\n", + " 'value_error': 0.13566998623386242},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2366659707960426,\n", + " 'value_error': 31.098393487878155},\n", + " 'HadISST': {'value': 18.72255668464148,\n", + " 'value_error': 27.35961845653863},\n", + " 'Tropflux': {'value': 0.6773365431140935,\n", + " 'value_error': 30.926575832045618}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r8i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r8i1p1': {'value': 1.2411398820922908,\n", + " 'value_error': 0.3721270870589959},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.358520138523886,\n", + " 'value_error': 37.4806132133739},\n", + " 'HadISST': {'value': 25.411665813984207,\n", + " 'value_error': 34.5236113722997},\n", + " 'Tropflux': {'value': 39.55134086275585,\n", + " 'value_error': 37.36143671898475}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r8i1p1': {'value': 10.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.92481203007519,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 79.59183673469387, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22172745207912886,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2448179083396378, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22177864304159578, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r8i1p1': {'value': -0.01718300409285816,\n", + " 'value_error': -0.00256149101395253},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 104.39484251449306,\n", + " 'value_error': -1.3500300560311551},\n", + " 'HadISST': {'value': 104.40703886484692,\n", + " 'value_error': -1.0156023019755878},\n", + " 'Tropflux': {'value': 104.32334030484331,\n", + " 'value_error': -1.3280656439318625}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14034258618988718,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14524224442386371, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14339227488726344, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8152981602824817,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1583270642339016, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5258440954967736,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7639943225915234, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3462315516957554,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3643633372853487, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3482674919513144, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5709874344209616,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.3850121708345107, 'value_error': None}}}}},\n", + " 'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,\n", + " 'name': 'CanCM4_r9i1p1',\n", + " 'nyears': 45,\n", + " 'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanCM4_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.758087139764898,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1905828409011843, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6966993997022868,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.95619604809908, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.913882425734805,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8680462076518465, 'value_error': None},\n", + " 'Tropflux': {'value': 0.944169798886861, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.199453589838721,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.9514502446920914, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanCM4_r9i1p1': {'value': 0.9912679386383292,\n", + " 'value_error': 0.14776949964742628},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 10.265298108685954,\n", + " 'value_error': 33.871854587065265},\n", + " 'HadISST': {'value': 29.31064036456654,\n", + " 'value_error': 29.799642810445825},\n", + " 'Tropflux': {'value': 9.656085769531066,\n", + " 'value_error': 33.684713645006035}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanCM4_r9i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanCM4_r9i1p1': {'value': 1.6428180433699013,\n", + " 'value_error': 0.49256099321908803},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 19.732724142952375,\n", + " 'value_error': 49.610707505183846},\n", + " 'HadISST': {'value': 1.2721587681773365,\n", + " 'value_error': 45.69671195247828},\n", + " 'Tropflux': {'value': 19.987948690543195,\n", + " 'value_error': 49.45296114786106}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r9i1p1': {'value': 11.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 66.9172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 77.55102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2102958162545779,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23444199235222377, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2102750979020648, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanCM4_r9i1p1': {'value': -0.42281082157617045,\n", + " 'value_error': -0.06302891591112346},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 208.14098420793704,\n", + " 'value_error': -33.219297048314274},\n", + " 'HadISST': {'value': 208.4410917377662,\n", + " 'value_error': -24.990254403269603},\n", + " 'Tropflux': {'value': 206.3815766070805,\n", + " 'value_error': -32.67883328103865}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30191648194785076,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2927635360139262, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3006179698491813, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8936523363573576,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.238569392155081, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5318305069472964,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7625538640190596, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3614458540649398,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.37965242426483253, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3635038634844897, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.650859833860903,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.481550991379502, 'value_error': None}}}}}},\n", + " 'CanESM2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 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1.0892786603240394,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9907826885961905, 'value_error': None},\n", + " 'Tropflux': {'value': 1.127318806453402, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5762162635058568,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.2908730863805955, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r1i1p1': {'value': 0.966594355823751,\n", + " 'value_error': 0.07738948483823736},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 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0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 103.78749975382875,\n", + " 'value_error': -0.9020989645302084},\n", + " 'HadISST': {'value': 103.79801063648509,\n", + " 'value_error': -0.6131619379023884},\n", + " 'Tropflux': {'value': 103.72587875135744,\n", + " 'value_error': -0.8874221998739196}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14454203525485798,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13521549721760434, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14517648944281705, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 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0.35441644956993745,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.37282033885866017, 'value_error': None},\n", + " 'Tropflux': {'value': 0.35543721480080437, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.77167540812989,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.5662867339427233, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 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0.9855097119336973, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.067673653469832,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.972552543016527, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1052952929311828, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.266246607807137,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.984759233380258, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': 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7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r2i1p1': {'value': 1.4170233205748748,\n", + " 'value_error': 0.2272707161703386},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.76494245513776,\n", + " 'value_error': 33.13784034937216},\n", + " 'HadISST': {'value': 14.841662483490142,\n", + " 'value_error': 27.5414423207404},\n", + " 'Tropflux': {'value': 30.98508803813702,\n", + " 'value_error': 33.032472499012215}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r2i1p1': {'value': 11.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 65.41353383458647,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 76.53061224489795, 'value_error': None},\n", + " 'Tropflux': {'value': 64.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22024617867265103,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24506165905942742, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22066086245334987, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r2i1p1': {'value': -0.16143923529187182,\n", + " 'value_error': -0.012925483349496251},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 141.2908017092808,\n", + " 'value_error': -9.834558914204582},\n", + " 'HadISST': {'value': 141.40538990723732,\n", + " 'value_error': -6.68460716545581},\n", + " 'Tropflux': {'value': 140.6190180103912,\n", + " 'value_error': -9.674554843301618}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09924358407157377,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10852601459445724, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10039154381304417, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8982004044488464,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2488609192822169, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5399213582562528,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.819430544561994, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.360686997293051,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3801224571818871, 'value_error': None},\n", + " 'Tropflux': {'value': 0.36095904359897435, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.8335961377929104,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.63538449851328, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7785395908194864,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1568629854066235, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7875790732562695,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0232779317904421, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0888277611393946,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9919529575214796, 'value_error': None},\n", + " 'Tropflux': {'value': 1.126671526166157, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.6877040508776364,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.3865831177184065, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r3i1p1': {'value': 0.9162259595504257,\n", + " 'value_error': 0.07335678568555205},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9178817621430773,\n", + " 'value_error': 24.27459315655393},\n", + " 'HadISST': {'value': 19.52143404421396,\n", + " 'value_error': 19.295889646914198},\n", + " 'Tropflux': {'value': 1.3547886384222645,\n", + " 'value_error': 24.14047678510622}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r3i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r3i1p1': {'value': 1.4495371208450454,\n", + " 'value_error': 0.23248547485887108},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.176334279095773,\n", + " 'value_error': 33.89819277749393},\n", + " 'HadISST': {'value': 12.887692399054067,\n", + " 'value_error': 28.173384605505113},\n", + " 'Tropflux': {'value': 29.401531133596396,\n", + " 'value_error': 33.790407246922406}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r3i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20931022663612658,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2328852243533117, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20929529295683344, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r3i1p1': {'value': -0.1989595480419749,\n", + " 'value_error': -0.01592951255492799},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 150.88725322265526,\n", + " 'value_error': -12.120222158044587},\n", + " 'HadISST': {'value': 151.02847301990693,\n", + " 'value_error': -8.238185829316775},\n", + " 'Tropflux': {'value': 150.05933935852306,\n", + " 'value_error': -11.923031322903574}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17736699583906157,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16800514258018323, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17752016354498157, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8794994040908481,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2296193500328023, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5541357621989083,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8264669501010798, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3696404164318324,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.38905091193193, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3698059515319395, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9142224116046944,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.7036142107950174, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", 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'metric': {'ERA-Interim': {'value': 1.0724398716525718,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9772555116789352, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1101786456997635, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.3563270942049974,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.095710711861572, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r4i1p1': {'value': 0.9443269414448262,\n", + " 'value_error': 0.07560666486098232},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", 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'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.8222262690421,\n", + " 'value_error': 34.06767880263357},\n", + " 'HadISST': {'value': 12.452143552750146,\n", + " 'value_error': 28.314247423852397},\n", + " 'Tropflux': {'value': 29.048549075005496,\n", + " 'value_error': 33.95935436011284}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r4i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18544956726923933,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20990238953457227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1855506806806446, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r4i1p1': {'value': 0.06232976321863539,\n", + " 'value_error': 0.004990374955654155},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 84.05811394612897,\n", + " 'value_error': 3.797005897444027},\n", + " 'HadISST': {'value': 84.01387280962105,\n", + " 'value_error': 2.580847097542165},\n", + " 'Tropflux': {'value': 84.31748161973213,\n", + " 'value_error': 3.7352302340783723}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1401991535732372,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12759838426081513, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14104209103805462, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9049317401043839,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2491848764877862, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5367981306361931,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.808426139532777, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3610997486283356,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3806663986395622, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3613253722597394, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9312711909983618,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.724099375819474, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,\n", + " 'name': 'CanESM2_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"CanESM2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7476219832228174,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1451935318875677, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7727741380719433,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0052256518871452, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0836516822242603,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9862274677638494, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1217009586155298, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.452012977378366,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.208611709777471, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'CanESM2_r5i1p1': {'value': 0.9779083324028184,\n", + " 'value_error': 0.07829532792913739},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.779220624734636,\n", + " 'value_error': 25.908812849103796},\n", + " 'HadISST': {'value': 27.567883265304943,\n", + " 'value_error': 20.59493192716525},\n", + " 'Tropflux': {'value': 8.178218817412446,\n", + " 'value_error': 25.765667464732996}}},\n", + " 'EnsoDuration': {'diagnostic': {'CanESM2_r5i1p1': {'value': 12.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'CanESM2_r5i1p1': {'value': 1.53960775325128,\n", + " 'value_error': 0.24693154419002047},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.77552779468458,\n", + " 'value_error': 36.00454218862},\n", + " 'HadISST': {'value': 7.474750210026541,\n", + " 'value_error': 29.924008671594958},\n", + " 'Tropflux': {'value': 25.014717821770493,\n", + " 'value_error': 35.89005913318816}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r5i1p1': {'value': 14.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 57.89473684210527,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 71.42857142857143, 'value_error': None},\n", + " 'Tropflux': {'value': 56.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20820055952074254,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2327979729139056, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2085107689366696, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'CanESM2_r5i1p1': {'value': -0.2322184908246773,\n", + " 'value_error': -0.01859235910757957},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 159.39378764111967,\n", + " 'value_error': -14.14629117174682},\n", + " 'HadISST': {'value': 159.55861435345935,\n", + " 'value_error': -9.615316777929058},\n", + " 'Tropflux': {'value': 158.42747609712157,\n", + " 'value_error': -13.916137059558945}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15000475707592115,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13820177742772205, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14998829864649232, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8630165457239232,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.210561726229579, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5221877838437499,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7881318530833019, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.36167412716822356,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.38096788462238707, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3622667255250927, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9604669521336615,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.769050056332188, 'value_error': None}}}}}},\n", + " 'EC-EARTH': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r1i1p1',\n", + " 'nyears': 60,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3736916150795229,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1733683773183137, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7386111379219993,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7144754188992224, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.76028795861894,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.5584138016330047, 'value_error': None},\n", + " 'Tropflux': {'value': 1.8095990467291043, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 0.4798685207490275,\n", + " 'value_error': 0.06195075964100573},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 46.62104620668935,\n", + " 'value_error': 15.331146957836234},\n", + " 'HadISST': {'value': 37.40127841308689,\n", + " 'value_error': 13.175671922792198},\n", + " 'Tropflux': {'value': 46.915963264547486,\n", + " 'value_error': 15.246442848199635}}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 16.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 1.024904255536285,\n", + " 'value_error': 0.26574573932618567},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.92368574340663,\n", + " 'value_error': 28.92059343597207},\n", + " 'HadISST': {'value': 38.40669998313239,\n", + " 'value_error': 26.011889049339093},\n", + " 'Tropflux': {'value': 50.082912582921104,\n", + " 'value_error': 28.828635096823984}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 8.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.6734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2451617758752234,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.251835955246885, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2455204811155478, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 0.29940790918241245,\n", + " 'value_error': 0.03865339486620887},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 23.421387707330265,\n", + " 'value_error': 21.994398081163073},\n", + " 'HadISST': {'value': 23.208870516542483,\n", + " 'value_error': 16.162865678493553},\n", + " 'Tropflux': {'value': 24.66728900476714,\n", + " 'value_error': 21.636558623313725}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18584378497223222,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1494106115331866, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18036417396574284, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.266494936828836,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.891337630001131, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3093260195659846,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8706605009977747, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14881981279633272,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.148678175648985, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1559504386749672, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 95,\n", + " 'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 95,\n", + " 'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 95,\n", + " 'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 95,\n", + " 'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r7i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2006425770092448,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0894109293480292, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1719059920885235,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.153339972406894, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9233200262415666,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.720794186588863, 'value_error': None},\n", + " 'Tropflux': {'value': 1.9727087917202113, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 0.4741352733642043,\n", + " 'value_error': 0.037137140757040984},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 47.258793285331656,\n", + " 'value_error': 12.470127118629813},\n", + " 'HadISST': {'value': 38.14917901775929,\n", + " 'value_error': 9.877878615554302},\n", + " 'Tropflux': {'value': 47.55018681043531,\n", + " 'value_error': 12.401230054532535}}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 17.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.76923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.76923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 1.044657267572688,\n", + " 'value_error': 0.16389989858337928},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 48.958563359626886,\n", + " 'value_error': 24.25159861807373},\n", + " 'HadISST': {'value': 37.21961036961689,\n", + " 'value_error': 20.084825871450214},\n", + " 'Tropflux': {'value': 49.12085898304041,\n", + " 'value_error': 24.174486205579797}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 9.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 72.93233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 81.63265306122449, 'value_error': None},\n", + " 'Tropflux': {'value': 71.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22181874888810055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2288668914331754, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22222899852535427, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 0.13665747076444262,\n", + " 'value_error': 0.010703839204517345},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 65.04755168577948,\n", + " 'value_error': 8.264154363090192},\n", + " 'HadISST': {'value': 64.95055337378992,\n", + " 'value_error': 5.597568048699975},\n", + " 'Tropflux': {'value': 65.61621308351874,\n", + " 'value_error': 8.129700103148236}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21142829035502406,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1669895034230152, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2082957124376765, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1779941583048683,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8111048902809108, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1837746546314178,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7402101282264294, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15859469679833837,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15834341566946364, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16590669608261155, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,\n", + " 'name': 'EC-EARTH_r8i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3493705919445533,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1585432904708968, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7163727173798784,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6933483490935772, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9914680995632086,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.789169307934207, 'value_error': None},\n", + " 'Tropflux': {'value': 2.040867315605399, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 0.503622016366456,\n", + " 'value_error': 0.03944672071630328},\n", + " 'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 43.97878756673273,\n", + " 'value_error': 13.245651434598596},\n", + " 'HadISST': {'value': 34.30264119356185,\n", + " 'value_error': 10.492189519017824},\n", + " 'Tropflux': {'value': 44.28830302131504,\n", + " 'value_error': 13.172469622800035}}},\n", + " 'EnsoDuration': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 18.0,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 38.46153846153847,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.46153846153847, 'value_error': None},\n", + " 'Tropflux': {'value': 38.46153846153847, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 1.0759538183452129,\n", + " 'value_error': 0.16881012288071753},\n", + " 'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 47.429429391096,\n", + " 'value_error': 24.97814445375148},\n", + " 'HadISST': {'value': 35.33879288758136,\n", + " 'value_error': 20.686540703821706},\n", + " 'Tropflux': {'value': 47.596587176843514,\n", + " 'value_error': 24.89872185531646}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 11.5,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': 33.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 65.41353383458647,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 76.53061224489795, 'value_error': None},\n", + " 'Tropflux': {'value': 64.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24502232383758646,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25347230099105333, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2455499992691495, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 0.0871994222931822,\n", + " 'value_error': 0.0068299858744047814},\n", + " 'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 77.69728004123601,\n", + " 'value_error': 5.27325350141523},\n", + " 'HadISST': {'value': 77.63538663195813,\n", + " 'value_error': 3.5717381374250823},\n", + " 'Tropflux': {'value': 78.06013576427785,\n", + " 'value_error': 5.187459920382212}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1916605638620769,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14797394305004477, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18691462301850095, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0214340415526952,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7311471793694729, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0631681596894047,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7440019280874054, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,\n", + " 'value_error': None},\n", + " 'ERA-Interim': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1612983971769656,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15968108246450102, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16917580329459034, 'value_error': None}}}}}},\n", + " 'FGOALS-g2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r1i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.861063606495195,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9915983189187882, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9283821687529638,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5249692786545384, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0245568631481512,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9132209455134684, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0603288378799165, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.230035767337718,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.903172869678766, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-g2_r1i1p1': {'value': 0.7450757742859876,\n", + " 'value_error': 0.0596538040906555},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 17.12028689431909,\n", + " 'value_error': 19.74012098551695},\n", + " 'HadISST': {'value': 2.8050623473267904,\n", + " 'value_error': 15.691434814034341},\n", + " 'Tropflux': {'value': 17.578194728929304,\n", + " 'value_error': 19.631057431642233}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-g2_r1i1p1': {'value': 1.5241371465475566,\n", + " 'value_error': 0.24445027531173671},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 25.531413975126327,\n", + " 'value_error': 35.64275386261847},\n", + " 'HadISST': {'value': 8.40448166054803,\n", + " 'value_error': 29.62332002659468},\n", + " 'Tropflux': {'value': 25.768200523255548,\n", + " 'value_error': 35.52942117962472}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 57.89473684210527,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 71.42857142857143, 'value_error': None},\n", + " 'Tropflux': {'value': 56.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14482378493585885,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1608688806610224, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14470906355170826, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-g2_r1i1p1': {'value': -0.3327165116486321,\n", + " 'value_error': -0.026638640375382112},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 185.09784801104644,\n", + " 'value_error': -20.268431832084204},\n", + " 'HadISST': {'value': 185.33400736494377,\n", + " 'value_error': -13.77657156149752},\n", + " 'Tropflux': {'value': 183.71334238902176,\n", + " 'value_error': -19.93867310754511}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08780714339088076,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10155044158658766, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09235453662717702, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9402482243911474,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0117398506235442, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5237938628628958,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.523582414235771, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2217419004746078,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22023009258660953, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22889081563218605, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6431253647456396,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9482625084995848, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r2i1p1',\n", + " 'nyears': 110,\n", + " 'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8637416125551207,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9924427581378525, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9321223085692444,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5303839816756724, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9920421576685735,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8914631439764118, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0262181314729437, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.047166621407168,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.722137017330548, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-g2_r2i1p1': {'value': 0.7675051840426104,\n", + " 'value_error': 0.07317874800366822},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 14.625315094249364,\n", + " 'value_error': 21.63907975369485},\n", + " 'HadISST': {'value': 0.12084822193087633,\n", + " 'value_error': 17.693867394853346},\n", + " 'Tropflux': {'value': 15.097007570378867,\n", + " 'value_error': 21.519524511746347}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': 11.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-g2_r2i1p1': {'value': 1.6393949969353427,\n", + " 'value_error': 0.31333574184989615},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 19.899972496197012,\n", + " 'value_error': 40.80062504287801},\n", + " 'HadISST': {'value': 1.4778723505695919,\n", + " 'value_error': 34.8923397251319},\n", + " 'Tropflux': {'value': 20.154665246927454,\n", + " 'value_error': 40.67089196103607}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': 11.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 66.9172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 77.55102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14572420861544483,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16098621959907033, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14535389258941123, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-g2_r2i1p1': {'value': -0.07609702488962236,\n", + " 'value_error': -0.007255566638514562},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 119.46309495150216,\n", + " 'value_error': -4.933118808862767},\n", + " 'HadISST': {'value': 119.51710797340598,\n", + " 'value_error': -3.449162952025784},\n", + " 'Tropflux': {'value': 119.14643871386315,\n", + " 'value_error': -4.8528590739268225}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10852335790170753,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12769614753547373, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11384366804719956, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9392202553694156,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.004248465537752, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5152705532925422,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.510360748551264, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22107946656752364,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21979344271364926, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22770632859278803, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7322748033605992,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0428032566669327, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r3i1p1',\n", + " 'nyears': 157,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.857439544903428,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9868608598115498, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9340216948484426,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5392609320030086, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0352168509319606,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9218209887448682, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0712820535982213, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.049626680470798,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.718836621928634, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-g2_r3i1p1': {'value': 0.7784069005735235,\n", + " 'value_error': 0.06212363381268215},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 13.412644961057344,\n", + " 'value_error': 20.60108616636798},\n", + " 'HadISST': {'value': 1.5429742594397875,\n", + " 'value_error': 16.367461648792126},\n", + " 'Tropflux': {'value': 13.891037401918608,\n", + " 'value_error': 20.487265806673545}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-g2_r3i1p1': {'value': 1.5957522357058769,\n", + " 'value_error': 0.2551173227346357},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 22.032336192168447,\n", + " 'value_error': 37.27749415657801},\n", + " 'HadISST': {'value': 4.100655572954056,\n", + " 'value_error': 30.96602004237174},\n", + " 'Tropflux': {'value': 22.28024870084143,\n", + " 'value_error': 37.15896351654014}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 54.88721804511278,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.38775510204081, 'value_error': None},\n", + " 'Tropflux': {'value': 53.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14087318032462032,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16286547256298606, 'value_error': None},\n", + " 'Tropflux': {'value': 0.141358716837897, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-g2_r3i1p1': {'value': -0.43795779825920694,\n", + " 'value_error': -0.03495283747409899},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 212.01507844273456,\n", + " 'value_error': -26.650915504849237},\n", + " 'HadISST': {'value': 212.32593716795597,\n", + " 'value_error': -18.105540852721685},\n", + " 'Tropflux': {'value': 210.192640984206,\n", + " 'value_error': -26.217316498399885}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10005571787103221,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10748255940919296, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1027634394550048, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9433079004056043,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0083414757969746, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5093513711556328,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.500844182890998, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22129477257802357,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21974445648985724, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2284588033011489, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6658927413075648,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9632543510773666, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r4i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r4i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8469668124497078,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.96971633944587, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9088377277830889,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5270873198909456, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.021234874875439,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9083591618390612, 'value_error': None},\n", + " 'Tropflux': {'value': 1.057053442219721, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.242771051641745,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.918006661821705, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-g2_r4i1p1': {'value': 0.7592088040005986,\n", + " 'value_error': 0.06002072600735617},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 15.548176394305138,\n", + " 'value_error': 20.02950863615771},\n", + " 'HadISST': {'value': 0.9614123598186228,\n", + " 'value_error': 15.889333789186901},\n", + " 'Tropflux': {'value': 16.014770090483324,\n", + " 'value_error': 19.91884622452273}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-g2_r4i1p1': {'value': 1.5331602094932795,\n", + " 'value_error': 0.24279447052835246},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 25.090551589019235,\n", + " 'value_error': 35.70215300478627},\n", + " 'HadISST': {'value': 7.86222591316001,\n", + " 'value_error': 29.612214869215325},\n", + " 'Tropflux': {'value': 25.328739940085487,\n", + " 'value_error': 35.58863145130919}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': 18.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 45.86466165413533,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 63.26530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14210184531612155,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1624076048601918, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14241189611480076, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-g2_r4i1p1': {'value': -0.5092867412581502,\n", + " 'value_error': -0.04026265211251505},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 230.25865619616712,\n", + " 'value_error': -30.89355288995196},\n", + " 'HadISST': {'value': 230.62014359926613,\n", + " 'value_error': -20.956145586210052},\n", + " 'Tropflux': {'value': 228.13940352367226,\n", + " 'value_error': -30.39092798626576}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1104832790611612,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11344733787135358, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11495641316285606, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9411859663788726,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0023353402784891, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5444016598771513,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.52956967504379, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22085264624049708,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21984840417407348, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22760450164477167, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6769866082304554,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9722860006826402, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-g2_r5i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-g2_r5i1p1',\n", + " 'nyears': 160,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-g2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8543434731906712,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9828893560262473, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9217811015459657,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5175935889140235, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0130190572834883,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9052201512708694, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0480563019914972, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.413437794217325,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 12.08299113428375, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-g2_r5i1p1': {'value': 0.7588925437922144,\n", + " 'value_error': 0.05999572344256182},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 15.583356111915403,\n", + " 'value_error': 20.02116503352596},\n", + " 'HadISST': {'value': 1.002668420338625,\n", + " 'value_error': 15.882714840633124},\n", + " 'Tropflux': {'value': 16.049755441249044,\n", + " 'value_error': 19.910548720035763}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-g2_r5i1p1': {'value': 1.5697521301090882,\n", + " 'value_error': 0.2485892439228871},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 23.302688472916984,\n", + " 'value_error': 36.554255962112805},\n", + " 'HadISST': {'value': 5.663174506642441,\n", + " 'value_error': 30.318969329084013},\n", + " 'Tropflux': {'value': 23.546561663166944,\n", + " 'value_error': 36.43802499076322}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': 18.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 45.86466165413533,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 63.26530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14462282195724663,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16187791648189315, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1445748905446516, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-g2_r5i1p1': {'value': -0.3901895585298711,\n", + " 'value_error': -0.03084719310424934},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 199.79754711523887,\n", + " 'value_error': -23.669066533659088},\n", + " 'HadISST': {'value': 200.07450034964046,\n", + " 'value_error': -16.055531260387294},\n", + " 'Tropflux': {'value': 198.17388367045487,\n", + " 'value_error': -23.283980935728653}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.06940694686790846,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07147166328244367, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07290308348287662, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9403164830797522,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9972728917761036, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5081961923499919,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.48473373153049937, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22235949345550612,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22036639196953836, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22958532147997773, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7081656597410955,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9881450533804974, 'value_error': None}}}}}},\n", + " 'FGOALS-s2': {'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r2i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6133030894386415,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.466903053977844, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1802483355457378,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7600665109807385, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0852276888006536,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9441886181186816, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1289839503130936, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.192729459461555,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.915409818735185, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-s2_r2i1p1': {'value': 1.072233972054954,\n", + " 'value_error': 0.08584742319612759},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 19.271686253979656,\n", + " 'value_error': 28.407886907113266},\n", + " 'HadISST': {'value': 39.87263800494682,\n", + " 'value_error': 22.581447496419894},\n", + " 'Tropflux': {'value': 18.612713900718397,\n", + " 'value_error': 28.25093421637577}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-s2_r2i1p1': {'value': 1.856435851514052,\n", + " 'value_error': 0.29774633866062555},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 9.295463848980999,\n", + " 'value_error': 43.41373495629271},\n", + " 'HadISST': {'value': 11.56555331555644,\n", + " 'value_error': 36.08191917821525},\n", + " 'Tropflux': {'value': 9.583875582858802,\n", + " 'value_error': 43.27569301148843}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': 28.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.285714285714285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 41.83673469387755, 'value_error': None},\n", + " 'Tropflux': {'value': 10.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.242207753440023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26968483888482364, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2462168120680919, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-s2_r2i1p1': {'value': 0.3852683933879022,\n", + " 'value_error': 0.03084615827632841},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4611237009867752,\n", + " 'value_error': 23.469788528817674},\n", + " 'HadISST': {'value': 1.1876634678028974,\n", + " 'value_error': 15.952552416445226},\n", + " 'Tropflux': {'value': 3.0643091094622963,\n", + " 'value_error': 23.087945098867948}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09791570018419933,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06483204828996168, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0927063125681455, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.644897526389196,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8915094507554705, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4359775052282495,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6629999122491101, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4205848296898124,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4383076808046799, 'value_error': None},\n", + " 'Tropflux': {'value': 0.41769583331032656, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.768536127967607,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.518449641686477, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FGOALS-s2_r3i1p1': {'keyerror': None,\n", + " 'name': 'FGOALS-s2_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FGOALS-s2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6565852060194719,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.504125716661884, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.098820399018865,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7432305956228085, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.054108370269925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9160521691806228, 'value_error': None},\n", + " 'Tropflux': {'value': 1.097293679001292, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.873971536789275,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.581408429281492, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FGOALS-s2_r3i1p1': {'value': 1.0944576744065575,\n", + " 'value_error': 0.0876267434102656},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 21.74377585696271,\n", + " 'value_error': 28.99668416546887},\n", + " 'HadISST': {'value': 42.77171409763771,\n", + " 'value_error': 23.04948281418455},\n", + " 'Tropflux': {'value': 21.071145285608672,\n", + " 'value_error': 28.83647838821002}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FGOALS-s2_r3i1p1': {'value': 1.908413805182375,\n", + " 'value_error': 0.3060828752466826},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 6.755846779142968,\n", + " 'value_error': 44.62926691355731},\n", + " 'HadISST': {'value': 14.689253580495492,\n", + " 'value_error': 37.09216918054087},\n", + " 'Tropflux': {'value': 7.0523336919873225,\n", + " 'value_error': 44.48735996161827}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': 30.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.774436090225564,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.775510204081634, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22365811078335057,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.250604747797424, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22760296713095843, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FGOALS-s2_r3i1p1': {'value': 0.42672636827263705,\n", + " 'value_error': 0.03416545276572354},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 9.142451180540904,\n", + " 'value_error': 25.995326361863054},\n", + " 'HadISST': {'value': 9.445337932156821,\n", + " 'value_error': 17.669175240374937},\n", + " 'Tropflux': {'value': 7.36675013999994,\n", + " 'value_error': 25.57239351061524}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16396201010632463,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13467538314051303, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15996443165172644, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6847990128962386,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9049124331934353, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4059741465867621,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6288327671302832, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4023962330570922,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4190055645330988, 'value_error': None},\n", + " 'Tropflux': {'value': 0.39984398973322904, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7393847304108518,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.541891901746267, 'value_error': None}}}}}},\n", + " 'FIO-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FIO-ESM_r1i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FIO-ESM_r1i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FIO-ESM_r1i1p1': {'value': 0.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 100.0, 'value_error': 0.0},\n", + " 'Tropflux': {'value': 100.0, 'value_error': 0.0}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FIO-ESM_r2i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FIO-ESM_r2i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FIO-ESM_r2i1p1': {'value': 0.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 100.0, 'value_error': 0.0},\n", + " 'Tropflux': {'value': 100.0, 'value_error': 0.0}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'FIO-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'FIO-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"FIO-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'FIO-ESM_r3i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'FIO-ESM_r3i1p1': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},\n", + " 'HadISST': {'value': nan, 'value_error': nan},\n", + " 'Tropflux': {'value': nan, 'value_error': nan}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'FIO-ESM_r3i1p1': {'value': 0.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},\n", + " 'HadISST': {'value': 100.0, 'value_error': 0.0},\n", + " 'Tropflux': {'value': 100.0, 'value_error': 0.0}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'GPCPv2.3': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'HadISST': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': nan, 'value_error': None},\n", + " 'Tropflux': {'value': nan, 'value_error': None}}}}}},\n", + " 'GFDL-CM2p1': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r10i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r10i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9488324535989932,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9285632550346732, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.270701927073681,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3142907760907518, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5458958018554556,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3789287946929094, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5943444719783966, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': 1.357153038524645,\n", + " 'value_error': 0.1127054253192404},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 50.965121072707895,\n", + " 'value_error': 36.40665241230291},\n", + " 'HadISST': {'value': 77.04025485320312,\n", + " 'value_error': 29.10972453145681},\n", + " 'Tropflux': {'value': 50.13104347878661,\n", + " 'value_error': 36.20550679117526}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': 1.150722280956384,\n", + " 'value_error': 0.1914558074134451},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 43.77627934320179,\n", + " 'value_error': 27.247187294892683},\n", + " 'HadISST': {'value': 30.845459657151846,\n", + " 'value_error': 22.780011603560492},\n", + " 'Tropflux': {'value': 43.955052990560404,\n", + " 'value_error': 27.16054985795204}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': 68.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 106.01503759398496,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 39.795918367346935, 'value_error': None},\n", + " 'Tropflux': {'value': 114.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3377114520449374,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3595476633592239, 'value_error': None},\n", + " 'Tropflux': {'value': 0.34046923786644867, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': 0.05577713940404991,\n", + " 'value_error': 0.004632039306678272},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 85.73405777796388,\n", + " 'value_error': 3.4403655368946016},\n", + " 'HadISST': {'value': 85.69446763818203,\n", + " 'value_error': 2.352177512812859},\n", + " 'Tropflux': {'value': 85.96615856160915,\n", + " 'value_error': 3.3843922597909017}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1319328194039181,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12212459723766149, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12895158725085473, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5066873462054486,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6576051310859168, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7460404631430588,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5996104545231352, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1408490686856091,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16828287434112263, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13189171291216722, 'value_error': None}}}}},\n", + " 'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9472372053192604,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9448888804785547, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.188172241276394,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2445984535164074, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6315994884546596,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.4627614432271676, 'value_error': None},\n", + " 'Tropflux': {'value': 1.6801542167072163, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': 1.51251592629888,\n", + " 'value_error': 0.1256076108859145},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 68.24716406805061,\n", + " 'value_error': 40.574378890016206},\n", + " 'HadISST': {'value': 97.30730246353102,\n", + " 'value_error': 32.44212016934013},\n", + " 'Tropflux': {'value': 67.31760372462286,\n", + " 'value_error': 40.35020670985323}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': 1.1432958471994998,\n", + " 'value_error': 0.1902202061787623},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.13913121800537,\n", + " 'value_error': 27.07134171090108},\n", + " 'HadISST': {'value': 31.291763358177864,\n", + " 'value_error': 22.632995898768296},\n", + " 'Tropflux': {'value': 44.3167511111771,\n", + " 'value_error': 26.985263407295175}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': 52.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 56.390977443609025,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29781603771310583,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3198466795035171, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3007476748325055, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': 0.1760404827960463,\n", + " 'value_error': 0.01461936636748172},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 54.97468347892023,\n", + " 'value_error': 10.858276644890633},\n", + " 'HadISST': {'value': 54.849731439507174,\n", + " 'value_error': 7.423802464626096},\n", + " 'Tropflux': {'value': 55.707226137916535,\n", + " 'value_error': 10.681617123977736}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13769705489168482,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13235458713907602, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1360508612214691, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4595841805864438,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6177700357811682, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7360543806333946,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6108174173747222, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15756142957798083,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1846348831943267, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14822906984096154, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r2i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9323881478767069,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9140332637108886, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.236259220180604,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2909706572282023, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6023614354146998,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.4351116421437915, 'value_error': None},\n", + " 'Tropflux': {'value': 1.6508882712504998, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': 1.3420510974823325,\n", + " 'value_error': 0.11145127737865866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 49.285232148490444,\n", + " 'value_error': 36.00153146965944},\n", + " 'HadISST': {'value': 75.07021063931285,\n", + " 'value_error': 28.78580133992793},\n", + " 'Tropflux': {'value': 48.46043588857539,\n", + " 'value_error': 35.80262412912723}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': 1.110069379783914,\n", + " 'value_error': 0.18469202596375817},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 45.76255995776478,\n", + " 'value_error': 26.284594295123494},\n", + " 'HadISST': {'value': 33.288562342057574,\n", + " 'value_error': 21.975235702586726},\n", + " 'Tropflux': {'value': 45.93501786105674,\n", + " 'value_error': 26.201017599440828}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': 68.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 106.01503759398496,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 39.795918367346935, 'value_error': None},\n", + " 'Tropflux': {'value': 114.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30471772382580364,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.32676194901437816, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3075857850964875, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': 0.22238960339598704,\n", + " 'value_error': 0.018468451328502663},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 43.12011575483705,\n", + " 'value_error': 13.71711664423685},\n", + " 'HadISST': {'value': 42.96226550330836,\n", + " 'value_error': 9.378391035834102},\n", + " 'Tropflux': {'value': 44.04552716485671,\n", + " 'value_error': 13.493945018211138}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12861969375159582,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12353953334454161, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12673965188692649, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4633688408412837,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6238232186893422, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7254740512841211,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5841313287479503, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16267485471387266,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1906087970043022, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1531278231438836, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r3i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9198217095113783,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9119430668629682, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.343386165886233,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3955269549002178, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6175962233338623,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.4493434560269771, 'value_error': None},\n", + " 'Tropflux': {'value': 1.666109568723837, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': 1.3862764795454416,\n", + " 'value_error': 0.11512399545380454},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 54.20471430571422,\n", + " 'value_error': 37.18791065230729},\n", + " 'HadISST': {'value': 80.83940003002793,\n", + " 'value_error': 29.734396415509863},\n", + " 'Tropflux': {'value': 53.352738060039016,\n", + " 'value_error': 36.98244860372657}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': 1.0392993246455051,\n", + " 'value_error': 0.17291738818064592},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.22034979708304,\n", + " 'value_error': 24.608877244069717},\n", + " 'HadISST': {'value': 37.54160472606977,\n", + " 'value_error': 20.574252421114423},\n", + " 'Tropflux': {'value': 49.38181302243208,\n", + " 'value_error': 24.530628798557228}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': 58.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 19.387755102040817, 'value_error': None},\n", + " 'Tropflux': {'value': 82.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29267789778986,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31459530462014146, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2955656912180566, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': 0.17171223209665087,\n", + " 'value_error': 0.014259924711223141},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 56.08170644674886,\n", + " 'value_error': 10.591307691295306},\n", + " 'HadISST': {'value': 55.95982656291838,\n", + " 'value_error': 7.241275822462098},\n", + " 'Tropflux': {'value': 56.79623831512599,\n", + " 'value_error': 10.418991641172772}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15074702931704578,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13768532805606323, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14870641555089992, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4560159520221876,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6051881774079328, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7100053935034153,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5398980248552918, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15860763692248964,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1862919415167865, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14932087710174358, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r4i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9192926924782492,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9188289788596655, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.188002303573358,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2369827929726989, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5822504587709305,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.4149841598417783, 'value_error': None},\n", + " 'Tropflux': {'value': 1.6307317971862214, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': 1.3684069839903044,\n", + " 'value_error': 0.11364001462068389},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 52.21697196316888,\n", + " 'value_error': 36.70854797544492},\n", + " 'HadISST': {'value': 78.50832906207074,\n", + " 'value_error': 29.35111164337273},\n", + " 'Tropflux': {'value': 51.37597793205174,\n", + " 'value_error': 36.50573439072982}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': 1.1010230805941434,\n", + " 'value_error': 0.18318691344082896},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.20455765524637,\n", + " 'value_error': 26.070392995271668},\n", + " 'HadISST': {'value': 33.832214509592326,\n", + " 'value_error': 21.79615270061254},\n", + " 'Tropflux': {'value': 46.375610145670244,\n", + " 'value_error': 25.987497391967736}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': 58.75, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 76.69172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 19.897959183673468, 'value_error': None},\n", + " 'Tropflux': {'value': 83.59375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.33225412616576006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.35414520923322007, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3350644449580544, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': 0.04877991937824491,\n", + " 'value_error': 0.0040509518119930974},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 87.52371457409139,\n", + " 'value_error': 3.008773044198564},\n", + " 'HadISST': {'value': 87.48909100164994,\n", + " 'value_error': 2.0570977763338063},\n", + " 'Tropflux': {'value': 87.72669840644296,\n", + " 'value_error': 2.9598215925173355}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11860405717158493,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10490506911705118, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11805029018865527, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4687924099574352,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6333954494550138, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7163995052366929,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5886975501139754, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1543647170126505,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18255784928803212, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14479451418293718, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r5i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9473622206700527,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9548273857946932, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2791293968355664,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.335515241546205, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5340215298672177,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3644912751325264, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5825230878235537, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': 1.4360899942139327,\n", + " 'value_error': 0.1192607826826507},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 59.74580146354938,\n", + " 'value_error': 38.524195700854506},\n", + " 'HadISST': {'value': 87.33755984082607,\n", + " 'value_error': 30.802851960891697},\n", + " 'Tropflux': {'value': 58.8632108838381,\n", + " 'value_error': 38.311350718983185}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': 1.1555567507910094,\n", + " 'value_error': 0.1922601607669188},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 43.54006954175449,\n", + " 'value_error': 27.36165948964855},\n", + " 'HadISST': {'value': 30.554924273638612,\n", + " 'value_error': 22.87571608478276},\n", + " 'Tropflux': {'value': 43.71959426156531,\n", + " 'value_error': 27.274658067337704}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': 70.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 112.03007518796993,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 43.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 120.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2972567922990421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31897645290444065, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3001561394916366, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': 0.1712728044858797,\n", + " 'value_error': 0.014223432234425668},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 56.194097454480854,\n", + " 'value_error': 10.564203547479224},\n", + " 'HadISST': {'value': 56.07252947263602,\n", + " 'value_error': 7.222744722523868},\n", + " 'Tropflux': {'value': 56.90680076919057,\n", + " 'value_error': 10.392328470194084}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14748135074687432,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1384244329945614, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1436578431338801, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4651746448033292,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6066845868924058, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7428202493086656,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5832133894668958, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1404703195995151,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16939927608973174, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13129454372013014, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r6i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r6i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8820769549994412,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.911618863697319, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.14930250641501,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2024446975773266, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5648372075956805,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3966442841319424, 'value_error': None},\n", + " 'Tropflux': {'value': 1.6132841430514944, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': 1.5049736646617946,\n", + " 'value_error': 0.12498126014908027},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 67.40818835279711,\n", + " 'value_error': 40.37205204106885},\n", + " 'HadISST': {'value': 96.32341642820906,\n", + " 'value_error': 32.28034537138632},\n", + " 'Tropflux': {'value': 66.48326332407628,\n", + " 'value_error': 40.14899771044726}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': 1.1450336924023938,\n", + " 'value_error': 0.19050934680112264},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.05422096220406,\n", + " 'value_error': 27.112490991241273},\n", + " 'HadISST': {'value': 31.187324704141133,\n", + " 'value_error': 22.667398755602015},\n", + " 'Tropflux': {'value': 44.23211084313097,\n", + " 'value_error': 27.026281845939966}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': 46.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 38.34586466165413,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31209328022688076,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3343474014032827, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3148651457676019, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': 0.08893002059499841,\n", + " 'value_error': 0.0073852362337146205},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 77.25465039677864,\n", + " 'value_error': 5.485254018389923},\n", + " 'HadISST': {'value': 77.19152862352571,\n", + " 'value_error': 3.750267527028312},\n", + " 'Tropflux': {'value': 77.62470751498556,\n", + " 'value_error': 5.396011279540536}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1246824374629104,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1237923981110762, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12394243229689493, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.462153420810742,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6151021277900597, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7347527630392011,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5918853939406852, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14785608124634078,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.176265866778398, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13922632663962278, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r7i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r7i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9385514562799606,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9290849476316065, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.246007288214062,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2961256528726688, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5044228174348913,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.337534274480946, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5528818618044127, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': 1.3021879430571623,\n", + " 'value_error': 0.10814082259093666},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 44.85098946302444,\n", + " 'value_error': 34.93217232885627},\n", + " 'HadISST': {'value': 69.87007268997975,\n", + " 'value_error': 27.930772163901402},\n", + " 'Tropflux': {'value': 44.050692255895065,\n", + " 'value_error': 34.73917316428457}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': 1.2073950788453292,\n", + " 'value_error': 0.20088496026619865},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.00727451025548,\n", + " 'value_error': 28.58910477069084},\n", + " 'HadISST': {'value': 27.439615038678095,\n", + " 'value_error': 23.901921741985408},\n", + " 'Tropflux': {'value': 41.194852717108844,\n", + " 'value_error': 28.498200460643968}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': 58.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 74.43609022556392,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 18.367346938775512, 'value_error': None},\n", + " 'Tropflux': {'value': 81.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3008446182685298,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.32267636309946596, 'value_error': None},\n", + " 'Tropflux': {'value': 0.30368014070415356, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': 0.2609110415018407,\n", + " 'value_error': 0.021667482640659423},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 33.267609581170746,\n", + " 'value_error': 16.093140755673705},\n", + " 'HadISST': {'value': 33.08241714007292,\n", + " 'value_error': 11.002878441282192},\n", + " 'Tropflux': {'value': 34.35331705633535,\n", + " 'value_error': 15.831312232708322}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12349792048655855,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11246219877818482, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12227780553103883, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.504876423346296,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6679861835749379, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7659482197192152,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6261682474529865, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17288629469458866,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20105210713864238, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16283370597658733, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 180,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r8i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r8i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9620828784049156,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9592086836022398, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.217038589730797,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2684953231062384, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5136171619631666,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3459445777763785, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5620688817931632, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': 1.3503188015150802,\n", + " 'value_error': 0.11213787282734688},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 50.204903626110905,\n", + " 'value_error': 36.2233188572449},\n", + " 'HadISST': {'value': 76.14873044323983,\n", + " 'value_error': 28.963136231476},\n", + " 'Tropflux': {'value': 49.37502620991418,\n", + " 'value_error': 36.02318614830119}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': 1.086647551357029,\n", + " 'value_error': 0.18079512994742175},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.90693889309762,\n", + " 'value_error': 25.730004402759736},\n", + " 'HadISST': {'value': 34.696135486034684,\n", + " 'value_error': 21.51157080952704},\n", + " 'Tropflux': {'value': 47.07575803335789,\n", + " 'value_error': 25.64819112751043}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': 62.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 86.46616541353383,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 26.53061224489796, 'value_error': None},\n", + " 'Tropflux': {'value': 93.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31324937069926556,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3349753829320318, 'value_error': None},\n", + " 'Tropflux': {'value': 0.31597548792730856, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': 0.0790147817656834,\n", + " 'value_error': 0.006561820467269721},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 79.79063961687886,\n", + " 'value_error': 4.873676473845252},\n", + " 'HadISST': {'value': 79.73455559592763,\n", + " 'value_error': 3.3321320317757475},\n", + " 'Tropflux': {'value': 80.1194372741865,\n", + " 'value_error': 4.794383840298369}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13117822876862903,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12408419239836321, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1292036522042564, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5145645370549583,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.672609277982109, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7498186187366267,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6081977786305208, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.156948572128813,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1849766428438231, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1474658244103659, 'value_error': None}}}}},\n", + " 'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM2p1_r9i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM2p1_r9i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9364112412320857,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.93710665469297, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.418965166211523,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4830506308401685, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5153493734595629,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3500085758262683, 'value_error': None},\n", + " 'Tropflux': {'value': 1.563713760684424, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': 1.300697778875719,\n", + " 'value_error': 0.10801707119142785},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 44.68522863157513,\n", + " 'value_error': 34.89219755235645},\n", + " 'HadISST': {'value': 69.6756811667197,\n", + " 'value_error': 27.89880946876157},\n", + " 'Tropflux': {'value': 43.885847247878864,\n", + " 'value_error': 34.699419247180394}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': 1.1902721487382337,\n", + " 'value_error': 0.19803606747669492},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.84389239373566,\n", + " 'value_error': 28.183662300872996},\n", + " 'HadISST': {'value': 28.468645570610402,\n", + " 'value_error': 23.562951555188988},\n", + " 'Tropflux': {'value': 42.02881042035321,\n", + " 'value_error': 28.094047169633157}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': 58.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 74.43609022556392,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 18.367346938775512, 'value_error': None},\n", + " 'Tropflux': {'value': 81.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2913618385348475,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3131898008058029, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29420281876660004, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': 0.0678650857313921,\n", + " 'value_error': 0.005635888609878658},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 82.64236204506268,\n", + " 'value_error': 4.185956910004713},\n", + " 'HadISST': {'value': 82.59419198365184,\n", + " 'value_error': 2.861938246888774},\n", + " 'Tropflux': {'value': 82.92476339709874,\n", + " 'value_error': 4.117853179876279}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12846288913166873,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11586543274891302, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12622528106359157, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4694214267637489,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6304413835245262, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7878498532906931,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6562246293555054, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19075368279036692,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2181676060725921, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18078769909153528, 'value_error': None}}}}}},\n", + " 'GFDL-CM3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9892403263305114,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1931645591513034, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2144234074470766,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3944037808577654, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1123145189085988,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.892286668278695, 'value_error': None},\n", + " 'Tropflux': {'value': 1.161932878469586, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.941860701176958,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.326227642799952, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM3_r1i1p1': {'value': 0.962005579781411,\n", + " 'value_error': 0.07961614826954308},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 7.010252124697273,\n", + " 'value_error': 25.77603796506816},\n", + " 'HadISST': {'value': 25.493373392769094,\n", + " 'value_error': 20.59841137197266},\n", + " 'Tropflux': {'value': 6.419023813264396,\n", + " 'value_error': 25.633626157797963}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM3_r1i1p1': {'value': 1.2063745763382567,\n", + " 'value_error': 0.2000242258991356},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.05713575726246,\n", + " 'value_error': 28.531181771060727},\n", + " 'HadISST': {'value': 27.500943808411023,\n", + " 'value_error': 23.840196162744704},\n", + " 'Tropflux': {'value': 41.24455542112213,\n", + " 'value_error': 28.440461637830843}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': 30.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.022556390977442,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.265306122448976, 'value_error': None},\n", + " 'Tropflux': {'value': 5.46875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19478037508530263,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2043205712080165, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19376448824136713, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM3_r1i1p1': {'value': -0.2969269111174794,\n", + " 'value_error': -0.024573846012533783},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 175.94405527835377,\n", + " 'value_error': -18.292984206737465},\n", + " 'HadISST': {'value': 176.1548114777885,\n", + " 'value_error': -12.500007708493865},\n", + " 'Tropflux': {'value': 174.7084779523727,\n", + " 'value_error': -17.995365170888885}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17616472068771283,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17018357831864964, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17651555392425283, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.021337314271504,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.343779350403467, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5698309357364779,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6202692794570769, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12459963233026906,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12703763887658784, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1308969458825784, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.924851650225163,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.1027529730411207, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0453487456629933,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.242800259060079, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.178692980548216,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3555327358611446, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.016213697148556,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7968925172194868, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0657715160475614, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.953066074921358,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.327084190326547, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM3_r2i1p1': {'value': 0.9327658354577885,\n", + " 'value_error': 0.07719624981119731},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 3.757721705026007,\n", + " 'value_error': 24.992586417993103},\n", + " 'HadISST': {'value': 21.679056480857593,\n", + " 'value_error': 19.972331550142382},\n", + " 'Tropflux': {'value': 3.1844635229006046,\n", + " 'value_error': 24.854503156129255}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM3_r2i1p1': {'value': 1.2499742071015154,\n", + " 'value_error': 0.20725330927337252},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 38.92687939450597,\n", + " 'value_error': 29.56232832774087},\n", + " 'HadISST': {'value': 24.88075258204044,\n", + " 'value_error': 24.701805625018284},\n", + " 'Tropflux': {'value': 39.12107259977005,\n", + " 'value_error': 29.468329474626476}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': 50.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 50.37593984962406,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.0408163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 56.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2308254483576096,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2405715599546331, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2299344961034623, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM3_r2i1p1': {'value': -0.4561437044697242,\n", + " 'value_error': -0.037750721586804196},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 216.66643005428332,\n", + " 'value_error': -28.101964724126958},\n", + " 'HadISST': {'value': 216.9901969812631,\n", + " 'value_error': -19.202704802316077},\n", + " 'Tropflux': {'value': 214.7683170927107,\n", + " 'value_error': -27.64475776696111}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16438884340152662,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1727905350095721, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16831837359050394, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0852570654910068,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4117184005782937, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5948966030845552,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6547027737774084, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1250001369073655,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1273079021166568, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13124862072140578, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.934493994381853,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.090726085972221, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.075626538748829,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2665223396957392, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2205976158910903,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3972870628465828, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0540960556091958,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8333276982362533, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1036655537450222, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.090545921983931,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.484085285053841, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM3_r3i1p1': {'value': 0.9248898166556159,\n", + " 'value_error': 0.07654442585725521},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8816199697836278,\n", + " 'value_error': 24.781555875213378},\n", + " 'HadISST': {'value': 20.65163191164241,\n", + " 'value_error': 19.803690662116136},\n", + " 'Tropflux': {'value': 2.313202222469716,\n", + " 'value_error': 24.644638550528363}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM3_r3i1p1': {'value': 1.1436363913858996,\n", + " 'value_error': 0.18962185409393484},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.12249239774065,\n", + " 'value_error': 27.04740169647116},\n", + " 'HadISST': {'value': 31.271297797489034,\n", + " 'value_error': 22.600373419879325},\n", + " 'Tropflux': {'value': 44.30016519709774,\n", + " 'value_error': 26.961399514538588}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': 44.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20139632983456518,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20837675222898733, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20025031384216777, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM3_r3i1p1': {'value': -0.22128622192686925,\n", + " 'value_error': -0.01831377803332474},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 156.5976758627417,\n", + " 'value_error': -13.632935282431276},\n", + " 'HadISST': {'value': 156.75474294347572,\n", + " 'value_error': -9.315691425405861},\n", + " 'Tropflux': {'value': 155.67685586250713,\n", + " 'value_error': -13.411133251188673}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19891949453350094,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2102987823308797, 'value_error': None},\n", + " 'Tropflux': {'value': 0.203741893113429, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.089431148747593,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.418014153311506, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5858503477664988,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6517926485398865, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11944812546291379,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12446172849220083, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1246827547991127, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.9495474128105577,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.0968323382811764, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0765199428873875,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2793483912543437, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2353507482834933,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4144042652975255, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0071897686570845,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.788694251411741, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0567533105703282, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.80842440141963,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.190739961991847, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM3_r4i1p1': {'value': 0.9446472918154742,\n", + " 'value_error': 0.0781795661358766},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 5.0793747880917435,\n", + " 'value_error': 25.310938906370144},\n", + " 'HadISST': {'value': 23.228989319587853,\n", + " 'value_error': 20.226736650172427},\n", + " 'Tropflux': {'value': 4.498814513829501,\n", + " 'value_error': 25.171096756919297}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM3_r4i1p1': {'value': 1.2025350423808452,\n", + " 'value_error': 0.19938760786796336},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.24473348457368,\n", + " 'value_error': 28.440375446554473},\n", + " 'HadISST': {'value': 27.731686890690497,\n", + " 'value_error': 23.764319859883575},\n", + " 'Tropflux': {'value': 41.43155664700022,\n", + " 'value_error': 28.349944048713237}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': 27.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.796992481203006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 44.89795918367347, 'value_error': None},\n", + " 'Tropflux': {'value': 15.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19134781789767794,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19769092895324594, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19002958094140684, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM3_r4i1p1': {'value': -0.2537990032866095,\n", + " 'value_error': -0.021004554964140974},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 164.91336693818025,\n", + " 'value_error': -15.635972978450422},\n", + " 'HadISST': {'value': 165.09351131496203,\n", + " 'value_error': -10.684412152307344},\n", + " 'Tropflux': {'value': 163.8572541977171,\n", + " 'value_error': -15.381582379857608}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21194614792629612,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2090689648278109, 'value_error': None},\n", + " 'Tropflux': {'value': 0.213891349757685, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.084029933330283,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.404982875454443, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5687788028484938,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6262927162874885, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12085193305750981,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12432163243902074, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12690164186810288, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.914105519042928,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.070202363890088, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-CM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-CM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-CM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0661039320235997,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2776490785764953, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1655048606445217,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3347062606627638, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9812070288096362,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7627145252593193, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0307312468082426, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.838392586620296,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.195871599181616, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-CM3_r5i1p1': {'value': 0.9768745350329162,\n", + " 'value_error': 0.08084671176189416},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.664224496308627,\n", + " 'value_error': 26.174437686565387},\n", + " 'HadISST': {'value': 27.433024671882784,\n", + " 'value_error': 20.916784636513967},\n", + " 'Tropflux': {'value': 8.06385803901581,\n", + " 'value_error': 26.029824733237316}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-CM3_r5i1p1': {'value': 1.0941834252570595,\n", + " 'value_error': 0.18142225219386646},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.53873982710923,\n", + " 'value_error': 25.877821705799654},\n", + " 'HadISST': {'value': 34.2432548003447,\n", + " 'value_error': 21.623091208810457},\n", + " 'Tropflux': {'value': 46.708729723932954,\n", + " 'value_error': 25.795538418283304}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': 42.75, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 28.57142857142857,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 12.755102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 33.59375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2255117757167929,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23504226489093874, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22469963406773572, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-CM3_r5i1p1': {'value': -0.32430943464557466,\n", + " 'value_error': -0.026840039784198237},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 182.94759656281389,\n", + " 'value_error': -19.979958514842128},\n", + " 'HadISST': {'value': 183.17778864486365,\n", + " 'value_error': -13.652755210870914},\n", + " 'Tropflux': {'value': 181.59807461297868,\n", + " 'value_error': -19.654893127900408}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18630067975095035,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19156747242965103, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18936323776165398, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0990194011772307,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4230280167888876, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5897447743258937,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6538060305861048, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11452763502509433,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1185365519698317, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12037714072126186, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.844475898902504,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.992179172115775, 'value_error': None}}}}}},\n", + " 'GFDL-ESM2G': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2G_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2G_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-ESM2G_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.058335974009238,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8610983105414902, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.3706118574720727,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.438094524035759, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8871664064015123,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.7222177644091083, 'value_error': None},\n", + " 'Tropflux': {'value': 1.9359093020780447, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.358431096041231,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 14.683522839573522, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': 0.7460660479284557,\n", + " 'value_error': 0.06195741295280196},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 17.010132198358775,\n", + " 'value_error': 20.0137836430586},\n", + " 'HadISST': {'value': 2.675881412634603,\n", + " 'value_error': 16.002452576077417},\n", + " 'Tropflux': {'value': 17.468648634234714,\n", + " 'value_error': 19.90320811152093}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': 1.5726231854152937,\n", + " 'value_error': 0.2616511791798713},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 23.16241013278425,\n", + " 'value_error': 37.23709811344601},\n", + " 'HadISST': {'value': 5.490633735263165,\n", + " 'value_error': 31.13207678747164},\n", + " 'Tropflux': {'value': 23.40672936378388,\n", + " 'value_error': 37.11869592004748}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': 39.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.293233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 20.408163265306122, 'value_error': None},\n", + " 'Tropflux': {'value': 21.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3186712647173022,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3383931015315869, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3213908479255624, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': 0.1935702712976709,\n", + " 'value_error': 0.016075136065333707},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 50.491145014942795,\n", + " 'value_error': 11.939523924231416},\n", + " 'HadISST': {'value': 50.353750480548086,\n", + " 'value_error': 8.16305110230171},\n", + " 'Tropflux': {'value': 51.296632928784256,\n", + " 'value_error': 11.745272972136242}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12738472429722278,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09403108448686227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1240550940100247, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4902323474145924,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6125094439662786, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8145794475125568,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6895057143952715, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2754825787321808,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2945048166060324, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2649943949621395, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.157801788936127,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.165980668540753, 'value_error': None}}}}}},\n", + " 'GFDL-ESM2M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GFDL-ESM2M_r1i1p1': {'keyerror': None,\n", + " 'name': 'GFDL-ESM2M_r1i1p1',\n", + " 'nyears': 145,\n", + " 'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GFDL-ESM2M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1350848944423784,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1595334757118128, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9906260317414441,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0654762438145235, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2539666191868981,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0832422348497441, 'value_error': None},\n", + " 'Tropflux': {'value': 1.3023020627649284, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.913390130656868,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.052477317145661, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': 1.3936112368795008,\n", + " 'value_error': 0.11573311389622222},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 55.02060794301463,\n", + " 'value_error': 37.384671042041944},\n", + " 'HadISST': {'value': 81.79621718391346,\n", + " 'value_error': 29.891720430885204},\n", + " 'Tropflux': {'value': 54.1641239103946,\n", + " 'value_error': 37.17812189843369}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': 1.0724947890484808,\n", + " 'value_error': 0.17844041015120676},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 47.59843584916293,\n", + " 'value_error': 25.39489056013855},\n", + " 'HadISST': {'value': 35.5466689190154,\n", + " 'value_error': 21.231398873216556},\n", + " 'Tropflux': {'value': 47.765056247832845,\n", + " 'value_error': 25.314142840907706}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': 55.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 65.41353383458647,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 12.244897959183673, 'value_error': None},\n", + " 'Tropflux': {'value': 71.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2148229410908963,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23591781045374116, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21789936003010973, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': 0.4743349161649029,\n", + " 'value_error': 0.03939137072429788},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 21.319138632834118,\n", + " 'value_error': 29.25724617568021},\n", + " 'HadISST': {'value': 21.655817527360277,\n", + " 'value_error': 20.003175768172255},\n", + " 'Tropflux': {'value': 19.345328090944204,\n", + " 'value_error': 28.78124328298743}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18271975802367652,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17598245275703817, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17980626338230615, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6478787216833868,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7621421895059985, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5293389956232548,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.403293422942314, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07253357190737085,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08065411559906702, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07736028532085737, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2274402787660854,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.258467860226432, 'value_error': None}}}}}},\n", + " 'GISS-E2-H': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.877054927142715,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4796559143491756, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5169820500340354,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.948666856596162, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8379585686657918,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9179864070288443, 'value_error': None},\n", + " 'Tropflux': {'value': 0.828891717362887, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.35556840902519,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.997659900296227, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r1i1p1': {'value': 0.5263559283690084,\n", + " 'value_error': 0.042142201527045994},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 41.44994396509105,\n", + " 'value_error': 13.945332899067154},\n", + " 'HadISST': {'value': 31.337008386875926,\n", + " 'value_error': 11.085154052817996},\n", + " 'Tropflux': {'value': 41.7734312018418,\n", + " 'value_error': 13.86828536896062}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r1i1p1': {'value': 1.410611439707683,\n", + " 'value_error': 0.22624234018278294},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 31.078223778292507,\n", + " 'value_error': 32.98789512163236},\n", + " 'HadISST': {'value': 15.226994966785092,\n", + " 'value_error': 27.416820203018595},\n", + " 'Tropflux': {'value': 31.297373225779285,\n", + " 'value_error': 32.88300404966692}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': 26.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30075187969925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 17.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2847528493350118,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2888497157885263, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2839854816528618, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r1i1p1': {'value': -0.12459193944040547,\n", + " 'value_error': -0.009975338620793675},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 131.8664855956953,\n", + " 'value_error': -7.589894528714784},\n", + " 'HadISST': {'value': 131.95491989603488,\n", + " 'value_error': -5.1588956652057645},\n", + " 'Tropflux': {'value': 131.34803149265286,\n", + " 'value_error': -7.466410188144669}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20398053098971128,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19529929638300522, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20558578634812627, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1423542107952447,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0331782595721748, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8110055651441949,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4937790334415139, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.312568720688009,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3070047802090827, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3222072115932682, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.9760209586824677,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.219806925605569, 'value_error': None}}}}},\n", + " 'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.96473243954701,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.5774165962889297, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4198470192394015,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9227782435341894, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8936282472631607,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0026213257055954, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8762968543512979, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.661834763592527,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.300428597186432, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r1i1p2': {'value': 0.5614181389609734,\n", + " 'value_error': 0.044949425052254265},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.549742059455596,\n", + " 'value_error': 14.874275032192255},\n", + " 'HadISST': {'value': 26.763152290538972,\n", + " 'value_error': 11.823570749384201},\n", + " 'Tropflux': {'value': 37.89477779030978,\n", + " 'value_error': 14.792095125721016}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r1i1p2': {'value': 1.437579945362024,\n", + " 'value_error': 0.230567711194757},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.76055595041248,\n", + " 'value_error': 33.61856790016655},\n", + " 'HadISST': {'value': 13.606278445410863,\n", + " 'value_error': 27.94098344872598},\n", + " 'Tropflux': {'value': 29.983895164795605,\n", + " 'value_error': 33.511671488255764}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': 19.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.224489795918366, 'value_error': None},\n", + " 'Tropflux': {'value': 40.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26972742837539454,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27400511201051764, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2688846192300261, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r1i1p2': {'value': -0.16546437646162582,\n", + " 'value_error': -0.01324775256165503},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 142.32029931308008,\n", + " 'value_error': -10.079762553210722},\n", + " 'HadISST': {'value': 142.43774452204968,\n", + " 'value_error': -6.851273511816183},\n", + " 'Tropflux': {'value': 141.63176612811492,\n", + " 'value_error': -9.915769123884722}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18072532384345766,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17385304558586503, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18223327553810337, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1140196157204267,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0086732060694632, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7883523001597664,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5017050087208084, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31732637869219116,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31128724881472286, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3270954451696716, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.918104775611529,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.1414648968549472, 'value_error': None}}}}},\n", + " 'r1i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8424279384144824,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.433728299230513, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3995461882262161,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9106956984081418, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9054398555581612,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0174608305976227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8877198893715517, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.225242054002862,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.865604694411903, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r1i1p3': {'value': 0.6073778303800954,\n", + " 'value_error': 0.04862914532045202},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 32.43734118601051,\n", + " 'value_error': 16.091936242476432},\n", + " 'HadISST': {'value': 20.767722703127575,\n", + " 'value_error': 12.791490425295542},\n", + " 'Tropflux': {'value': 32.81062277252582,\n", + " 'value_error': 16.003028795727882}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r1i1p3': {'value': 1.4816214000814536,\n", + " 'value_error': 0.23763134438267636},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.608712288007464,\n", + " 'value_error': 34.64850062890561},\n", + " 'HadISST': {'value': 10.959535084692048,\n", + " 'value_error': 28.79697866578747},\n", + " 'Tropflux': {'value': 27.838893684579524,\n", + " 'value_error': 34.53832935669921}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': 26.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.804511278195488,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 46.93877551020408, 'value_error': None},\n", + " 'Tropflux': {'value': 18.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2692814474640175,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2746202321848834, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2685340623392114, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r1i1p3': {'value': -0.14266446096694135,\n", + " 'value_error': -0.011422298374117117},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 136.48883716586826,\n", + " 'value_error': -8.69083679568981},\n", + " 'HadISST': {'value': 136.5900991884806,\n", + " 'value_error': -5.9072125577859556},\n", + " 'Tropflux': {'value': 135.89517937806227,\n", + " 'value_error': -8.549440594904924}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20686262028728356,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21107263749948416, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21045369597145952, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0943542610755412,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9954380395523758, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.711381840963713,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4300565078777371, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2624906753532569,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2591388467027778, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2716528146163393, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.365349930813731,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.591069995997823, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8573881771483727,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4632515308662826, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4866972439052395,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9089861219474377, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8319746447049507,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9093017830918447, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8235001697940697, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.246844192963287,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.88943785474221, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r2i1p1': {'value': 0.5312923609245989,\n", + " 'value_error': 0.04253743244280102},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 40.90083187731051,\n", + " 'value_error': 14.076119295897174},\n", + " 'HadISST': {'value': 30.69305206588691,\n", + " 'value_error': 11.189116243420901},\n", + " 'Tropflux': {'value': 41.227352941250714,\n", + " 'value_error': 13.99834917501994}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r2i1p1': {'value': 1.3827566748612186,\n", + " 'value_error': 0.22177482559534192},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.43919379130625,\n", + " 'value_error': 32.33649670281385},\n", + " 'HadISST': {'value': 16.90097268599151,\n", + " 'value_error': 26.875431512911884},\n", + " 'Tropflux': {'value': 32.65401578464503,\n", + " 'value_error': 32.23367687177403}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': 29.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.781954887218044,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 40.816326530612244, 'value_error': None},\n", + " 'Tropflux': {'value': 9.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2752442237623956,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2786150913595279, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2745105075529668, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r2i1p1': {'value': -0.19296491647500572,\n", + " 'value_error': -0.015449557912147754},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 149.35402530006004,\n", + " 'value_error': -11.755041059362332},\n", + " 'HadISST': {'value': 149.49099015874344,\n", + " 'value_error': -7.989970102486909},\n", + " 'Tropflux': {'value': 148.55105639902965,\n", + " 'value_error': -11.563791564642967}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21778633171698655,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2101732142831879, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2198507137862772, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.134106105537351,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0155905700268244, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8127660850340769,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5014900835475666, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31286835574001814,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30740927093477355, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32247988079635076, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.042669951575294,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.2696002016048773, 'value_error': None}}}}},\n", + " 'r2i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.9282840273018955,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.537521171468076, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4167504249194254,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8825531335789034, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8730987544117929,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9832362281866838, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8559287946141841, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.381182053495696,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.02000683371365, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r2i1p2': {'value': 0.5249885557936571,\n", + " 'value_error': 0.04203272410401865},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 41.60204587293028,\n", + " 'value_error': 13.90910557695944},\n", + " 'HadISST': {'value': 31.515381777604674,\n", + " 'value_error': 11.056356931272632},\n", + " 'Tropflux': {'value': 41.924692751375616,\n", + " 'value_error': 13.83225820167969}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r2i1p2': {'value': 1.2782416808098718,\n", + " 'value_error': 0.20501208273592886},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 37.5457446309285,\n", + " 'value_error': 29.892358249549694},\n", + " 'HadISST': {'value': 23.18197244776665,\n", + " 'value_error': 24.844065029013855},\n", + " 'Tropflux': {'value': 37.744329407868996,\n", + " 'value_error': 29.79731000567692}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': 18.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 44.3609022556391,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 62.244897959183675, 'value_error': None},\n", + " 'Tropflux': {'value': 42.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2756450739599309,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2798538739811807, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27489881599900723, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r2i1p2': {'value': -0.1127389622149645,\n", + " 'value_error': -0.00902634094069187},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 128.83488716551528,\n", + " 'value_error': -6.867834599345227},\n", + " 'HadISST': {'value': 128.91490832329856,\n", + " 'value_error': -4.668107311619226},\n", + " 'Tropflux': {'value': 128.36575587342995,\n", + " 'value_error': -6.756097865265952}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15618631746435582,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13171236411505408, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15554279320894998, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.096818966798927,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0020051076543641, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7766631710991125,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.47824072024112163, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.306502791789241,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30082009059607967, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3161949265296, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.048116652766058,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.273043210235894, 'value_error': None}}}}},\n", + " 'r2i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8363056870525005,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4111239310419488, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3938859699519333,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8429439784304773, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8808241201529512,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.990367574083921, 'value_error': None},\n", + " 'Tropflux': {'value': 0.864172500483183, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.79653760419905,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.43910528810911, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r2i1p3': {'value': 0.5508012911147663,\n", + " 'value_error': 0.04409939692983293},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.730724362126146,\n", + " 'value_error': 14.592991076651238},\n", + " 'HadISST': {'value': 28.148117283487373,\n", + " 'value_error': 11.599977952975097},\n", + " 'Tropflux': {'value': 39.06923520252566,\n", + " 'value_error': 14.51236525527713}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r2i1p3': {'value': 1.3703148281186135,\n", + " 'value_error': 0.219779327441842},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 33.04709626029255,\n", + " 'value_error': 32.045537531555794},\n", + " 'HadISST': {'value': 17.64868584557847,\n", + " 'value_error': 26.633610224970113},\n", + " 'Tropflux': {'value': 33.2599853160678,\n", + " 'value_error': 31.94364285864622}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': 24.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 27.819548872180448,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 51.02040816326531, 'value_error': None},\n", + " 'Tropflux': {'value': 25.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3129816656186353,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3214059216781522, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3128235509816313, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r2i1p3': {'value': -0.2796040321997107,\n", + " 'value_error': -0.022386238736298903},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 171.51343742307034,\n", + " 'value_error': -17.03292463164718},\n", + " 'HadISST': {'value': 171.7118979901886,\n", + " 'value_error': -11.577378409612836},\n", + " 'Tropflux': {'value': 170.34994435624574,\n", + " 'value_error': -16.75580622662034}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17601358290473687,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16967043675631888, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17935104202312888, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1001750727713524,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9999350040223601, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7410503509971499,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.47161958714317365, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2586004593808654,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2556371659230464, 'value_error': None},\n", + " 'Tropflux': {'value': 0.26762388345051796, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.394385961314243,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.626211253866261, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8668305284145594,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4700443019661997, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4744379485510126,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9141673973477011, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8364711159278028,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9174712534284283, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8270233294829065, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.329445331549383,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.971779210832125, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r3i1p1': {'value': 0.5632568245764126,\n", + " 'value_error': 0.04509663771876839},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.345212880601025,\n", + " 'value_error': 14.922989374754101},\n", + " 'HadISST': {'value': 26.523296238412275,\n", + " 'value_error': 11.862293811485925},\n", + " 'Tropflux': {'value': 37.69137862880076,\n", + " 'value_error': 14.840540323056834}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r3i1p1': {'value': 1.3109285696617519,\n", + " 'value_error': 0.21025460241142246},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 35.94867943252067,\n", + " 'value_error': 30.656758445766535},\n", + " 'HadISST': {'value': 21.2176003214958,\n", + " 'value_error': 25.47937148508071},\n", + " 'Tropflux': {'value': 36.15234237161859,\n", + " 'value_error': 30.559279651060002}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': 19.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 41.35338345864661,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 60.204081632653065, 'value_error': None},\n", + " 'Tropflux': {'value': 39.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26926946828181964,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2741372747576348, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2686003237232434, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r3i1p1': {'value': -0.2443587862835401,\n", + " 'value_error': -0.01956436065681756},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 162.49887254552783,\n", + " 'value_error': -14.885853959629081},\n", + " 'HadISST': {'value': 162.6723163364654,\n", + " 'value_error': -10.118001926730376},\n", + " 'Tropflux': {'value': 161.4820425970401,\n", + " 'value_error': -14.643667476921909}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18344935102910606,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17504020590421243, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1856739392989135, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1377064798267722,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0227620585507011, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8245268985144806,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.518045351304265, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3132976082808482,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3077521796991147, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32292638872326546, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.9537569162868813,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.182448736707369, 'value_error': None}}}}},\n", + " 'r3i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.9586007541069383,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.5607533328212866, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.411300338028962,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8918256245768972, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8792430885495005,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9894735950331593, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8618847350861056, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.518516601266207,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.15846253727753, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r3i1p2': {'value': 0.5627550413323499,\n", + " 'value_error': 0.045056462906527345},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.40102955421252,\n", + " 'value_error': 14.909695073304299},\n", + " 'HadISST': {'value': 26.588753729858116,\n", + " 'value_error': 11.851726162749083},\n", + " 'Tropflux': {'value': 37.7468869170283,\n", + " 'value_error': 14.827319472208647}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r3i1p2': {'value': 1.6225379733709195,\n", + " 'value_error': 0.26023239128624753},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 20.72359831771452,\n", + " 'value_error': 37.94390927916867},\n", + " 'HadISST': {'value': 2.4909227932683993,\n", + " 'value_error': 31.535850792262917},\n", + " 'Tropflux': {'value': 20.975672198856614,\n", + " 'value_error': 37.82325964983715}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': 23.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.82706766917293,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 53.06122448979592, 'value_error': None},\n", + " 'Tropflux': {'value': 28.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26648709370163337,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27161829593047676, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2657217680758711, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r3i1p2': {'value': -0.06454233093185574,\n", + " 'value_error': -0.0051675221471975145},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 116.50778748761958,\n", + " 'value_error': -3.931791146444942},\n", + " 'HadISST': {'value': 116.55359908589695,\n", + " 'value_error': -2.6724614189499034},\n", + " 'Tropflux': {'value': 116.23921283951766,\n", + " 'value_error': -3.867822584676221}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21987586557345717,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21973112449812504, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22199216044855805, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1106704248031949,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0040652834593002, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7951421319973709,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5086937029422983, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3119138277471764,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30553433389782736, 'value_error': None},\n", + " 'Tropflux': {'value': 0.321727112552947, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.934766564742194,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.171378533284638, 'value_error': None}}}}},\n", + " 'r3i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.871812426150658,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4531261679452983, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.386620040535656,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8675062836776725, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.911378659301356,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0269310299667072, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8927604698195025, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.117859422789614,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.75930758169495, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r3i1p3': {'value': 0.5801215729954268,\n", + " 'value_error': 0.04644689823313039},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 35.469235216575036,\n", + " 'value_error': 15.369805907610345},\n", + " 'HadISST': {'value': 24.32329426856574,\n", + " 'value_error': 12.217468559618956},\n", + " 'Tropflux': {'value': 35.8257656829636,\n", + " 'value_error': 15.28488820847981}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r3i1p3': {'value': 1.4683916115936912,\n", + " 'value_error': 0.23550947139672154},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 28.25511320036858,\n", + " 'value_error': 34.33911502289767},\n", + " 'HadISST': {'value': 11.754600894093011,\n", + " 'value_error': 28.539842843630726},\n", + " 'Tropflux': {'value': 28.483239246504766,\n", + " 'value_error': 34.22992749905549}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': 22.75, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 31.57894736842105,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 53.57142857142857, 'value_error': None},\n", + " 'Tropflux': {'value': 28.90625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28205775816059964,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2878059366970713, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2813594379882682, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r3i1p3': {'value': -0.08542149512979527,\n", + " 'value_error': -0.00683919315520218},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 121.84798516133861,\n", + " 'value_error': -5.203708533892612},\n", + " 'HadISST': {'value': 121.90861661301753,\n", + " 'value_error': -3.5369911000645544},\n", + " 'Tropflux': {'value': 121.49252777912776,\n", + " 'value_error': -5.119046419762338}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14878873691955757,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14714121086306917, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15163699341123585, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1020629635483958,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0094414522698394, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7048095477776456,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.42412451779149146, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.257990977994072,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25493336938761596, 'value_error': None},\n", + " 'Tropflux': {'value': 0.26704783161410744, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2583996699885978,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.495479656502281, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.881651477911025,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4810844713456177, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4987670818031287,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9450442987421107, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8444630821808545,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9249228807536983, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8351179290237398, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.51209272473677,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.15438018961078, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r4i1p1': {'value': 0.5462831021854537,\n", + " 'value_error': 0.04373765230393623},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 39.233312441274926,\n", + " 'value_error': 14.473285673284005},\n", + " 'HadISST': {'value': 28.737513834070928,\n", + " 'value_error': 11.504824051172639},\n", + " 'Tropflux': {'value': 39.56904649818425,\n", + " 'value_error': 14.39332122053671}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r4i1p1': {'value': 1.4236518620132823,\n", + " 'value_error': 0.22833384148239094},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.44107520379255,\n", + " 'value_error': 33.29285229924327},\n", + " 'HadISST': {'value': 14.443309428280665,\n", + " 'value_error': 27.67027548039685},\n", + " 'Tropflux': {'value': 30.662250582207456,\n", + " 'value_error': 33.18699155991512}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': 26.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30075187969925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 17.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24873906435532142,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25204870250422756, 'value_error': None},\n", + " 'Tropflux': {'value': 0.247858042062786, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r4i1p1': {'value': -0.03939060162320159,\n", + " 'value_error': -0.00315377215759747},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 110.07480937265552,\n", + " 'value_error': -2.399597542870934},\n", + " 'HadISST': {'value': 110.10276848710664,\n", + " 'value_error': -1.631020472724735},\n", + " 'Tropflux': {'value': 109.91089652946049,\n", + " 'value_error': -2.3605571162755385}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24539697634495145,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24233220522865423, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2483360977838597, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1450623300563674,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0188692186379624, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8094694938483057,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.461087415021992, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31415525108135106,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30852954554679735, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3238148668022025, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.0681849395744694,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.30180116307324, 'value_error': None}}}}},\n", + " 'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.953363841552582,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.5633142701440508, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.40964015705149,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9086444301471407, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8909244122833557,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9995528611496547, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8739177127396536, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.496020230292665,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.1353369420029, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r4i1p2': {'value': 0.574544964606292,\n", + " 'value_error': 0.04600041223020734},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.089558302286875,\n", + " 'value_error': 15.222058620569829},\n", + " 'HadISST': {'value': 25.050761357687986,\n", + " 'value_error': 12.100024146525053},\n", + " 'Tropflux': {'value': 36.44266150293944,\n", + " 'value_error': 15.137957220600365}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r4i1p2': {'value': 1.5389964715650548,\n", + " 'value_error': 0.24683350315950747},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.805394715085548,\n", + " 'value_error': 35.99024704285034},\n", + " 'HadISST': {'value': 7.5114861842320355,\n", + " 'value_error': 29.91212772435969},\n", + " 'Tropflux': {'value': 25.0444897748119,\n", + " 'value_error': 35.87580944146029}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': 24.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 50.0, 'value_error': None},\n", + " 'Tropflux': {'value': 23.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2701288708039832,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27361309151244395, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2692935930471113, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r4i1p2': {'value': 0.04683675987077994,\n", + " 'value_error': 0.0037499419441600914},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 88.02071032464222,\n", + " 'value_error': 2.8532027760591716},\n", + " 'HadISST': {'value': 87.98746599031412,\n", + " 'value_error': 1.9393386005137503},\n", + " 'Tropflux': {'value': 88.21560824851503,\n", + " 'value_error': 2.806782386160268}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17272106490241207,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18079902800706033, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17730089227461127, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1232241957370264,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.012272961384686, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7868527473709617,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4887319529900594, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31473270270790643,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30875202604054813, 'value_error': None},\n", + " 'Tropflux': {'value': 0.324480183849281, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.827234619427585,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.056315211434957, 'value_error': None}}}}},\n", + " 'r4i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8549542223722484,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4418867725798763, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3878067228047641,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8702191700789493, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9210794710016766,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.03909252856951, 'value_error': None},\n", + " 'Tropflux': {'value': 0.901828668011448, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.223390433697244,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.86392625288629, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r4i1p3': {'value': 0.5564514881686649,\n", + " 'value_error': 0.0445517747412708},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.10221552184551,\n", + " 'value_error': 14.74268803001528},\n", + " 'HadISST': {'value': 27.411050572513894,\n", + " 'value_error': 11.718972157078248},\n", + " 'Tropflux': {'value': 38.444198854020186,\n", + " 'value_error': 14.66123513763436}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r4i1p3': {'value': 1.439764823918492,\n", + " 'value_error': 0.23091813514831078},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.653803866385925,\n", + " 'value_error': 33.66966244161533},\n", + " 'HadISST': {'value': 13.474974589756036,\n", + " 'value_error': 27.98344902135776},\n", + " 'Tropflux': {'value': 29.877482518629115,\n", + " 'value_error': 33.56260356522464}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': 22.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 33.83458646616541,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 55.10204081632652, 'value_error': None},\n", + " 'Tropflux': {'value': 31.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29829553398625847,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3055252384088153, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29787102783382025, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r4i1p3': {'value': -0.23114930442864673,\n", + " 'value_error': -0.018506755685744624},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 159.1203252241989,\n", + " 'value_error': -14.08115845117231},\n", + " 'HadISST': {'value': 159.28439303711625,\n", + " 'value_error': -9.571045687130145},\n", + " 'Tropflux': {'value': 158.1584627968646,\n", + " 'value_error': -13.85206402051482}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21364381135833166,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21062673527804715, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21632362603506308, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0971029697765777,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0050677351465525, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7327816855360407,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4802009776551229, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2553469192321254,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25285503990999464, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2642929019375272, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.161944218128198,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.379604111852496, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.874509689973064,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.472616806531931, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4866354513867346,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9037877194460897, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8248458336584693,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9051245239666483, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8157171071456946, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.279772272664104,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.924000677956194, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r5i1p1': {'value': 0.5107396648885939,\n", + " 'value_error': 0.04089189980681971},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 43.18704438052166,\n", + " 'value_error': 13.531593865959406},\n", + " 'HadISST': {'value': 33.374145826757314,\n", + " 'value_error': 10.756272630419673},\n", + " 'Tropflux': {'value': 43.50093419155841,\n", + " 'value_error': 13.456832231129834}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r5i1p1': {'value': 1.3367977372910322,\n", + " 'value_error': 0.2144036549841436},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.684724716006684,\n", + " 'value_error': 31.261722622730442},\n", + " 'HadISST': {'value': 19.662950319439588,\n", + " 'value_error': 25.982167859566868},\n", + " 'Tropflux': {'value': 34.89240663129699,\n", + " 'value_error': 31.162320233299578}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': 26.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.804511278195488,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 46.93877551020408, 'value_error': None},\n", + " 'Tropflux': {'value': 18.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2720762452665185,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27779061070693156, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2715140285819595, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r5i1p1': {'value': -0.31561592876108574,\n", + " 'value_error': -0.02526949799199528},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 180.72408610709405,\n", + " 'value_error': -19.22669814466453},\n", + " 'HadISST': {'value': 180.94810761250898,\n", + " 'value_error': -13.068499086445856},\n", + " 'Tropflux': {'value': 179.41073972219408,\n", + " 'value_error': -18.913887982052618}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12767930015830276,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10549589674735305, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12762064484851884, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1362679546920644,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0240498506257734, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8088737145075731,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4972784325969952, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30498603583443595,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29979739473304956, 'value_error': None},\n", + " 'Tropflux': {'value': 0.31454356893867563, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.979487722866061,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.2149898143768434, 'value_error': None}}}}},\n", + " 'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.933847143807274,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.5406603762702247, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3983313098559613,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9029186077357367, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8906007009936836,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0048660374227858, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8720984734449079, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.44197081941096,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.082520982434517, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r5i1p2': {'value': 0.5491804718575567,\n", + " 'value_error': 0.04396962753217858},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.91101882299816,\n", + " 'value_error': 14.550048909777487},\n", + " 'HadISST': {'value': 28.359552726817554,\n", + " 'value_error': 11.565843197024662},\n", + " 'Tropflux': {'value': 39.24853354211448,\n", + " 'value_error': 14.46966034253844}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r5i1p2': {'value': 1.4369670917770814,\n", + " 'value_error': 0.2304694180536809},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 29.79049967300616,\n", + " 'value_error': 33.60423599471342},\n", + " 'HadISST': {'value': 13.64310888544453,\n", + " 'value_error': 27.92907194987083},\n", + " 'Tropflux': {'value': 30.013743675823047,\n", + " 'value_error': 33.497385153728594}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': 25.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 24.06015037593985,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 48.46938775510204, 'value_error': None},\n", + " 'Tropflux': {'value': 21.09375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2661708093861126,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2705190984294822, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2652842580396545, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r5i1p2': {'value': -0.15925471455928467,\n", + " 'value_error': -0.012750581713567192},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 140.7320737629052,\n", + " 'value_error': -9.701482231791607},\n", + " 'HadISST': {'value': 140.84511140660095,\n", + " 'value_error': -6.5941541667424675},\n", + " 'Tropflux': {'value': 140.06938032894027,\n", + " 'value_error': -9.54364326164345}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23087317745808206,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22854301146887854, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23359679551651058, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0974707775129107,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0008723822860062, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7572684414374798,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4781972233441372, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3143502390978442,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30798884351101125, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32415578608416806, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7602776287337614,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.9865854798168723, 'value_error': None}}}}},\n", + " 'r5i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.854587110988177,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4333493815661, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3815424707290171,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8434825655940893, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9052855373115822,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0246321847919961, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8857353009891487, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.002177967020014,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.644853900334724, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r5i1p3': {'value': 0.5181188420496381,\n", + " 'value_error': 0.04148270681451905},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 42.36620963166754,\n", + " 'value_error': 13.727098611865513},\n", + " 'HadISST': {'value': 32.411534118192456,\n", + " 'value_error': 10.911679478152307},\n", + " 'Tropflux': {'value': 42.6846345291366,\n", + " 'value_error': 13.651256819401464}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r5i1p3': {'value': 1.529107566548678,\n", + " 'value_error': 0.24524746114271082},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 25.288561715884633,\n", + " 'value_error': 35.758989765073245},\n", + " 'HadISST': {'value': 8.105776129094542,\n", + " 'value_error': 29.719925730807933},\n", + " 'Tropflux': {'value': 25.526120457380763,\n", + " 'value_error': 35.64528748867669}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': 21.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 36.84210526315789,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 57.14285714285714, 'value_error': None},\n", + " 'Tropflux': {'value': 34.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29018492447717426,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.297230730189407, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2897651493538964, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r5i1p3': {'value': -0.08985128978536577,\n", + " 'value_error': -0.007193860575168252},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 122.98097970510801,\n", + " 'value_error': -5.473562862917858},\n", + " 'HadISST': {'value': 123.04475538740706,\n", + " 'value_error': -3.720412664485303},\n", + " 'Tropflux': {'value': 122.60708898583592,\n", + " 'value_error': -5.384510334171989}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15565567752985968,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14858946170795315, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15775128229391366, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1250309337034505,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0190848102713737, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7641759876990963,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4877050573629663, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26693538577562603,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26338847218658556, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2761348573200171, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3847516016729178,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.6070304108780893, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.88373577400808,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.4800494007263327, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4731889937317975,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9185258307357356, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8472502805984422,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9282429281059803, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8378590451055146, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.2518484553162,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 20.893964848653056, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r6i1p1': {'value': 0.5718953293778648,\n", + " 'value_error': 0.04578827163151485},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.38429477761678,\n", + " 'value_error': 15.151858888164401},\n", + " 'HadISST': {'value': 25.396405572299713,\n", + " 'value_error': 12.044222334276252},\n", + " 'Tropflux': {'value': 36.735769568419194,\n", + " 'value_error': 15.06814533953095}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r6i1p1': {'value': 1.3574262106721808,\n", + " 'value_error': 0.2177121735178629},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 33.67682772456757,\n", + " 'value_error': 31.744130390923313},\n", + " 'HadISST': {'value': 18.42324842241681,\n", + " 'value_error': 26.383105445803167},\n", + " 'Tropflux': {'value': 33.88771443347909,\n", + " 'value_error': 31.643194097381755}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': 24.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 50.0, 'value_error': None},\n", + " 'Tropflux': {'value': 23.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24547828643383598,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2478760822542714, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24443804701207175, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r6i1p1': {'value': 0.13779379634480354,\n", + " 'value_error': 0.011032333107243778},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 64.75691729240516,\n", + " 'value_error': 8.39412554026826},\n", + " 'HadISST': {'value': 64.65911242617052,\n", + " 'value_error': 5.705536183546382},\n", + " 'Tropflux': {'value': 65.33030718752698,\n", + " 'value_error': 8.257556704814585}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1836444791769145,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1818787319833921, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18742288680339608, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1302449532033683,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.007871789350852, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8212291770903363,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5024912345474117, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3162832287927244,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.310981637805239, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32587453212023026, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.988846531139811,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.2208811179954737, 'value_error': None}}}}},\n", + " 'r6i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.938670594630599,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.544983781424655, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.417272330796148,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.93021509562013, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8990346860438385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0096889654555121, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8814577668755026, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.490105713517266,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.128961899110838, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r6i1p2': {'value': 0.6290716951124901,\n", + " 'value_error': 0.05036604457469984},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 30.024188930594146,\n", + " 'value_error': 16.66669592362604},\n", + " 'HadISST': {'value': 17.937763787698806,\n", + " 'value_error': 13.248367263948719},\n", + " 'Tropflux': {'value': 30.410803108191832,\n", + " 'value_error': 16.57461294753316}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r6i1p2': {'value': 1.3888428837041653,\n", + " 'value_error': 0.22275096834642777},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.14182464194002,\n", + " 'value_error': 32.47882591789598},\n", + " 'HadISST': {'value': 16.53521199644029,\n", + " 'value_error': 26.993723828476597},\n", + " 'Tropflux': {'value': 32.35759217511187,\n", + " 'value_error': 32.37555352497289}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': 22.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 33.08270676691729,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.59183673469388, 'value_error': None},\n", + " 'Tropflux': {'value': 30.46875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2576223993787478,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2630670402317045, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25685633336715635, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r6i1p2': {'value': -0.13238583491595993,\n", + " 'value_error': -0.010599349667518852},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 133.8599055474323,\n", + " 'value_error': -8.064683226065311},\n", + " 'HadISST': {'value': 133.95387188857958,\n", + " 'value_error': -5.48161231737841},\n", + " 'Tropflux': {'value': 133.30901935362917,\n", + " 'value_error': -7.933474276282164}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23074461702185373,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23212675115343098, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23394693596087554, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1037684467641389,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9893599796158047, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7750619269001221,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4841721974423219, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3098124708754561,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30390572529450255, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3195418931091673, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8209255170051266,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.0435493567821545, 'value_error': None}}}}},\n", + " 'r6i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-H_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.854118508455846,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.444390432593787, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3772804819275812,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8737427191314163, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9328148475075659,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0564784475261675, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9118339866122649, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.424626629430463,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 21.064941585878262, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H_r6i1p3': {'value': 0.5602474433852408,\n", + " 'value_error': 0.04485569439164934},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.67996628201038,\n", + " 'value_error': 14.843258492533163},\n", + " 'HadISST': {'value': 26.91586921869656,\n", + " 'value_error': 11.798915682144507},\n", + " 'Tropflux': {'value': 38.02428252808924,\n", + " 'value_error': 14.7612499514779}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H_r6i1p3': {'value': 1.3844763577513584,\n", + " 'value_error': 0.22205063867220037},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.355170937109726,\n", + " 'value_error': 32.37671239738821},\n", + " 'HadISST': {'value': 16.797625525889387,\n", + " 'value_error': 26.908855484445066},\n", + " 'Tropflux': {'value': 32.57026009654728,\n", + " 'value_error': 32.27376469316042}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': 20.75, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 37.59398496240601,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 57.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 35.15625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28741795907507783,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2957362933613719, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2871424365087507, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H_r6i1p3': {'value': -0.16288932165598086,\n", + " 'value_error': -0.013041583175667606},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 141.6616857042,\n", + " 'value_error': -9.922895307478072},\n", + " 'HadISST': {'value': 141.77730316114094,\n", + " 'value_error': -6.744649928186497},\n", + " 'Tropflux': {'value': 140.98386788120405,\n", + " 'value_error': -9.761454041205603}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17582801268391457,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17258756484357785, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1789459195610404, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1020724940513276,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0162122751357048, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7431415094164189,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4924735769527339, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2709286185213397,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2679116379915146, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27999278290809104, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.291508625862517,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5199947096203106, 'value_error': None}}}}}},\n", + " 'GISS-E2-H-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-H-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-H-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-H-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.0072200108231435,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 3.6817134771260718, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4695821519964256,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1443227084106575, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.007027014091788,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1127810373266107, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9895379967118211, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 22.52602485828862,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 22.160086607051902, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': 0.7299916859637086,\n", + " 'value_error': 0.05753140441760243},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.79818994224637,\n", + " 'value_error': 19.238732646740168},\n", + " 'HadISST': {'value': 4.772777678611451,\n", + " 'value_error': 15.254437935471202},\n", + " 'Tropflux': {'value': 19.246827414755035,\n", + " 'value_error': 19.13243924782745}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': 1.4972797720905207,\n", + " 'value_error': 0.23637253630118119},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 26.84365198775298,\n", + " 'value_error': 34.83047023781109},\n", + " 'HadISST': {'value': 10.018519570588698,\n", + " 'value_error': 28.874740883694766},\n", + " 'Tropflux': {'value': 27.076266034080838,\n", + " 'value_error': 34.71972035980783}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': 18.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 45.86466165413533,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 63.26530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 43.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31599395513735207,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3122873132917546, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3145457520122913, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': -0.45022822367594995,\n", + " 'value_error': -0.03548295482615616},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 215.15344627416818,\n", + " 'value_error': -27.282721464506803},\n", + " 'HadISST': {'value': 215.47301444312623,\n", + " 'value_error': -18.49760907757573},\n", + " 'Tropflux': {'value': 213.2799489121502,\n", + " 'value_error': -26.838843245085325}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2409562384608357,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23080631607412005, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24172100187950576, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1495380363326455,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1012846508874976, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7102362967668185,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.46976376923820495, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29775742183503084,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29449845357093235, 'value_error': None},\n", + " 'Tropflux': {'value': 0.30699658627043314, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.700492633864462,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.889043408379172, 'value_error': None}}}}}},\n", + " 'GISS-E2-R': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0600938069058357,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.857231853699034, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7031611553098582,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.160113920743273, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0042314572088702,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1751441967269578, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9710083463998387, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.050339774207675,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.667180078640083, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p1': {'value': 0.5493077268957681,\n", + " 'value_error': 0.043979816089344176},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.896863402271556,\n", + " 'value_error': 14.553420419007832},\n", + " 'HadISST': {'value': 28.342952340747026,\n", + " 'value_error': 11.568523211871156},\n", + " 'Tropflux': {'value': 39.2344563296467,\n", + " 'value_error': 14.473013224285253}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p1': {'value': 1.720108498453369,\n", + " 'value_error': 0.27588133847760926},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 15.956350792087878,\n", + " 'value_error': 40.22564765004806},\n", + " 'HadISST': {'value': 3.3727377308671183,\n", + " 'value_error': 33.43224370954563},\n", + " 'Tropflux': {'value': 16.22358301241603,\n", + " 'value_error': 40.09774281444212}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': 20.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 39.849624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 59.183673469387756, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11216266773259204,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12221951265178949, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11051634424869443, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p1': {'value': 0.11805815754765699,\n", + " 'value_error': 0.009452217404868212},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 69.8046390974898,\n", + " 'value_error': 7.191869458535124},\n", + " 'HadISST': {'value': 69.72084242004021,\n", + " 'value_error': 4.888355699014591},\n", + " 'Tropflux': {'value': 70.29590471590076,\n", + " 'value_error': 7.0748608157676}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09736622451405222,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08997614545872855, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09849358272100879, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6779942600495655,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8462222262243452, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3047824002582374,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5370826929622878, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2799197933067166,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28183761403930263, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28770059015835603, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.972957423336851,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1115480738965924, 'value_error': None}}}}},\n", + " 'r1i1p121': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p121': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p121',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p121; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1177535681197672,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.903306773800081, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.703122477851472,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.129075385840092, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.015930472664537,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1870554128659132, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9827750074395032, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.824647552844567,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.443484507595706, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p121': {'value': 0.5646268026661776,\n", + " 'value_error': 0.04535191615178908},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 37.1928211441369,\n", + " 'value_error': 14.97548093479134},\n", + " 'HadISST': {'value': 26.344582959017515,\n", + " 'value_error': 11.910138295961973},\n", + " 'Tropflux': {'value': 37.539828851936186,\n", + " 'value_error': 14.892741868859089}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p121': {'value': 1.7637211651202624,\n", + " 'value_error': 0.28379019476105377},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 13.825457501536906,\n", + " 'value_error': 41.290210271556745},\n", + " 'HadISST': {'value': 5.993712371219275,\n", + " 'value_error': 34.33483295706703},\n", + " 'Tropflux': {'value': 14.099465288498383,\n", + " 'value_error': 41.158920463550096}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': 18.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 44.3609022556391,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 62.244897959183675, 'value_error': None},\n", + " 'Tropflux': {'value': 42.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1355279619772293,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14677684115974465, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13437910468397812, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p121': {'value': 0.13228360174820136,\n", + " 'value_error': 0.010625274582099572},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 66.16624230597148,\n", + " 'value_error': 8.067179620693556},\n", + " 'HadISST': {'value': 66.07234852905829,\n", + " 'value_error': 5.486127664599041},\n", + " 'Tropflux': {'value': 66.71670308537358,\n", + " 'value_error': 7.935930055636596}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17182777986235304,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16554138043179212, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17507017179740428, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7065060657502106,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8767616278830698, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25121066306900686,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4976123957180021, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2785936612498321,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27978014969890913, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2865492668811042, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9711333168010132,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1366015491904156, 'value_error': None}}}}},\n", + " 'r1i1p122': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p122': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p122',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p122; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0276391313236553,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8255586972236224, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5188913449238177,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.938982271916212, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9623854788205982,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1324882950931314, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9302583421468877, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.573240504656123,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.185824852575962, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p122': {'value': 0.47673602424980893,\n", + " 'value_error': 0.03829235894615769},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 46.96949453922114,\n", + " 'value_error': 12.64437183705962},\n", + " 'HadISST': {'value': 37.80991175273686,\n", + " 'value_error': 10.056185701193655},\n", + " 'Tropflux': {'value': 47.2624864308903,\n", + " 'value_error': 12.574512076317795}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p122': {'value': 1.6787629751016722,\n", + " 'value_error': 0.2701200626740023},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 17.97647258324927,\n", + " 'value_error': 39.301266894603366},\n", + " 'HadISST': {'value': 0.8880107815942353,\n", + " 'value_error': 32.68092908591619},\n", + " 'Tropflux': {'value': 18.23728145527361,\n", + " 'value_error': 39.176301297405494}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': 30.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.774436090225564,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.775510204081634, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1649874658317104,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17114059832375725, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16403050918119141, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p122': {'value': -0.23175518922702468,\n", + " 'value_error': -0.01861502475606024},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 159.27529045940494,\n", + " 'value_error': -14.133352243318797},\n", + " 'HadISST': {'value': 159.43978832420586,\n", + " 'value_error': -9.611460061791469},\n", + " 'Tropflux': {'value': 158.3109068139156,\n", + " 'value_error': -13.903408641967216}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11262120687883155,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09578905421973509, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11288000619952122, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7026828432288843,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8591294792020746, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3010607602177117,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4980442801100818, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2732587544191353,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2747590654591607, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2810456957631204, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0353152828697367,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.2028232606824396, 'value_error': None}}}}},\n", + " 'r1i1p123': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p123': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p123',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p123; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0622104440944593,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8530332425778475, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6123816681675616,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0760472998377217, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9876794616087884,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1606188667824393, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9543639800962639, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.50811319505804,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.123179771138133, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p123': {'value': 0.5017815321614212,\n", + " 'value_error': 0.04030406255183644},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 44.1835168146203,\n", + " 'value_error': 13.308648708900343},\n", + " 'HadISST': {'value': 34.542731871225484,\n", + " 'value_error': 10.584491232407114},\n", + " 'Tropflux': {'value': 44.49190114648188,\n", + " 'value_error': 13.235118839122558}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p123': {'value': 1.659605956794708,\n", + " 'value_error': 0.2670376174077723},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 18.912475008624007,\n", + " 'value_error': 38.85278482741862},\n", + " 'HadISST': {'value': 0.26326161252033925,\n", + " 'value_error': 32.30799427256079},\n", + " 'Tropflux': {'value': 19.170307689006,\n", + " 'value_error': 38.7292452613334}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': 22.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.08163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17215903697917911,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1820206867195128, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17142799466882538, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p123': {'value': -0.18403007961436835,\n", + " 'value_error': -0.014781651704572638},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 147.06879038519963,\n", + " 'value_error': -11.222885438858494},\n", + " 'HadISST': {'value': 147.19941337257126,\n", + " 'value_error': -7.632181899707592},\n", + " 'Tropflux': {'value': 146.30300127967783,\n", + " 'value_error': -11.040293888677091}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16109079401347343,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16491186175727762, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16562010956997272, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6950061971403831,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8608713728372996, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29997476331465917,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5125873369647761, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28086833576572967,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2815850456848359, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2888952452737016, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9819717877525254,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.163389222486997, 'value_error': None}}}}},\n", + " 'r1i1p124': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p124': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p124',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p124; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0880912399107734,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8726880782676596, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.62248889741277,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0665733408351716, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0115527010350103,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1842792395681963, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9782102098271656, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.621288040843954,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.23647641199294, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p124': {'value': 0.5177059399314778,\n", + " 'value_error': 0.0415831417640599},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 42.41213946897387,\n", + " 'value_error': 13.731008511573966},\n", + " 'HadISST': {'value': 32.465397090277,\n", + " 'value_error': 10.920397884247016},\n", + " 'Tropflux': {'value': 42.73031060552175,\n", + " 'value_error': 13.655145117035758}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p124': {'value': 1.7034889597531655,\n", + " 'value_error': 0.27409857817785616},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 16.7683732207737,\n", + " 'value_error': 39.88012319321893},\n", + " 'HadISST': {'value': 2.373959330080846,\n", + " 'value_error': 33.16227646071517},\n", + " 'Tropflux': {'value': 17.0330234666324,\n", + " 'value_error': 39.75331701609161}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': 26.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30075187969925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 17.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13623497316209357,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14518593656654433, 'value_error': None},\n", + " 'Tropflux': {'value': 0.134911286295595, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p124': {'value': -0.054578782932330955,\n", + " 'value_error': -0.004383873339922203},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 113.95944238411677,\n", + " 'value_error': -3.3284310343473433},\n", + " 'HadISST': {'value': 113.99818194065377,\n", + " 'value_error': -2.263516921130991},\n", + " 'Tropflux': {'value': 113.73232822185697,\n", + " 'value_error': -3.2742788837667867}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1325880015662856,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12513338456275516, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13601015021397664, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7067036278869547,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8588511809422174, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3063735715656406,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.49525049417350686, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28639661800916255,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2876414870839543, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29434250820636504, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8805015574010109,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.031996131092176, 'value_error': None}}}}},\n", + " 'r1i1p125': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p125': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p125',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p125; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0883231697762707,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8850926153741656, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6803983691817101,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1092667084540073, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0082345193690327,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.179337719794677, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9751074999196638, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.060786047170726,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.677343087032778, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p125': {'value': 0.5095375296045861,\n", + " 'value_error': 0.04092703925796328},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 43.32076585005711,\n", + " 'value_error': 13.514359439053331},\n", + " 'HadISST': {'value': 33.53096405654231,\n", + " 'value_error': 10.748094875973138},\n", + " 'Tropflux': {'value': 43.63391685413783,\n", + " 'value_error': 13.439693023895858}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p125': {'value': 1.6038775940344587,\n", + " 'value_error': 0.25807068814808554},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 21.63533520899213,\n", + " 'value_error': 37.548136529282786},\n", + " 'HadISST': {'value': 3.612348854962116,\n", + " 'value_error': 31.223115288181468},\n", + " 'Tropflux': {'value': 21.884510055214786,\n", + " 'value_error': 37.42874533210759}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': 25.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.308270676691727,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 47.95918367346938, 'value_error': None},\n", + " 'Tropflux': {'value': 20.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12123265776038133,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13129371587915478, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11968711178172518, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p125': {'value': -0.04331654460729214,\n", + " 'value_error': -0.0034792685889844348},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 111.07893537080554,\n", + " 'value_error': -2.6416149944266354},\n", + " 'HadISST': {'value': 111.10968108624006,\n", + " 'value_error': -1.7964440835020947},\n", + " 'Tropflux': {'value': 110.89868582671673,\n", + " 'value_error': -2.598637047316466}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13528568310809302,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1101509968767725, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13550024365548685, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7006115452497297,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8873973432788488, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.27544840116190866,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5408701063084709, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2837043935186768,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28543763196930244, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2914023089162169, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8632894910654654,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.033086679779805, 'value_error': None}}}}},\n", + " 'r1i1p126': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p126': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p126',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p126; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.108449692147058,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8924783697656657, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6701120019865148,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.116500194981419, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0166991454504684,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1889259705042041, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9832355400290084, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.79551757401917,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.411471293198343, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p126': {'value': 0.5297230726246832,\n", + " 'value_error': 0.04254838108977677},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 41.07539420850662,\n", + " 'value_error': 14.049736458402151},\n", + " 'HadISST': {'value': 30.897765309470138,\n", + " 'value_error': 11.173885164023961},\n", + " 'Tropflux': {'value': 41.40095082100162,\n", + " 'value_error': 13.972112101880697}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p126': {'value': 1.8391392829893518,\n", + " 'value_error': 0.2959252888914922},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 10.140565619536506,\n", + " 'value_error': 43.05581245782263},\n", + " 'HadISST': {'value': 10.526087698500612,\n", + " 'value_error': 35.80301768500558},\n", + " 'Tropflux': {'value': 10.426290197101162,\n", + " 'value_error': 42.91890859334772}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13892991846928898,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14718425571904142, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13765641027284994, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p126': {'value': -0.3187068349855984,\n", + " 'value_error': -0.02559914901137718},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 181.51463739896306,\n", + " 'value_error': -19.4360090759059},\n", + " 'HadISST': {'value': 181.7408528033622,\n", + " 'value_error': -13.217559555412286},\n", + " 'Tropflux': {'value': 180.188429082373,\n", + " 'value_error': -19.119793513888155}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16877714588318052,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1601923519630566, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1693308232138842, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7048906357782685,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8627111753417117, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2790387396437008,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.49783703011575964, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28352387771746884,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2846629746549678, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2915074972176249, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9405127366894974,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0952923710106885, 'value_error': None}}}}},\n", + " 'r1i1p127': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p127': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p127',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p127; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1335013604453534,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9197711022684567, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.713027782496193,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.163798113845931, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0414209053367802,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.214728916999408, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0076439759615934, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.179309057479852,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.791160421190867, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p127': {'value': 0.49482378282388456,\n", + " 'value_error': 0.039745202676479637},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", 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'GISS-E2-R_r1i1p127': {'value': 1.758324110998657,\n", + " 'value_error': 0.2829217859271653},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.089154892579488,\n", + " 'value_error': 41.16386065125672},\n", + " 'HadISST': {'value': 5.669367563473912,\n", + " 'value_error': 34.2297670569745},\n", + " 'Tropflux': {'value': 14.362324205248534,\n", + " 'value_error': 41.032972595076515}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': 29.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.278195488721805,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 39.795918367346935, 'value_error': None},\n", + " 'Tropflux': {'value': 7.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1352255602618883,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14322179632509136, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13384892885404986, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p127': {'value': -0.021671769109071096,\n", + " 'value_error': -0.0017407183839917991},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 105.54291972056326,\n", + " 'value_error': -1.3216305860333264},\n", + " 'HadISST': {'value': 105.55830215819839,\n", + " 'value_error': -0.8987817876050112},\n", + " 'Tropflux': {'value': 105.452738785382,\n", + " 'value_error': -1.3001282211748706}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19691415407569168,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18465023451214047, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19857518235554533, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6853335575279447,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8556407599039675, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28996172979546514,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5389573036189109, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2828283510498223,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28496302747683316, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2904681459876099, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8328895229028088,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9791783109834877, 'value_error': None}}}}},\n", + " 'r1i1p128': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p128': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p128',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p128; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.101914950664987,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.887739735011123, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6525655054389239,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0987088470467934, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9963313939233454,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.167229323245907, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9634683282318709, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.75693253640443,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.371447788343307, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p128': {'value': 0.5335937489114218,\n", + " 'value_error': 0.042859281290720065},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 40.64483324915014,\n", + " 'value_error': 14.15239761204043},\n", + " 'HadISST': {'value': 30.392836611816858,\n", + " 'value_error': 11.255532527656488},\n", + " 'Tropflux': {'value': 40.97276869754091,\n", + " 'value_error': 14.074206055841277}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p128': {'value': 1.688191347129516,\n", + " 'value_error': 0.27163712760862824},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 17.515806996152712,\n", + " 'value_error': 39.52199309058434},\n", + " 'HadISST': {'value': 1.4546242421635476,\n", + " 'value_error': 32.864473732901835},\n", + " 'Tropflux': {'value': 17.77808063902921,\n", + " 'value_error': 39.39632565390197}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': 28.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.037593984962406,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 42.3469387755102, 'value_error': None},\n", + " 'Tropflux': {'value': 11.71875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12562398493374966,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13745686420748926, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12448944187075632, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p128': {'value': -0.2754431780948853,\n", + " 'value_error': -0.022124128466014522},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 170.4492289518584,\n", + " 'value_error': -16.79761938456848},\n", + " 'HadISST': {'value': 170.644736180075,\n", + " 'value_error': -11.423308856133236},\n", + " 'Tropflux': {'value': 169.303050102089,\n", + " 'value_error': -16.52432929535792}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13126507467961052,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12581016552478239, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1331000343508005, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.699769142868899,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8583514706081451, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2960218843091938,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.49601379030319537, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2864593199044741,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2874547684493358, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2944344425190296, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.962705169651321,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.127547293245924, 'value_error': None}}}}},\n", + " 'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1686648053822224,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.963435155922053, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.761216295579251,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.227907640553879, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1149053337117873,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2943900892444988, 'value_error': None},\n", + " 'Tropflux': {'value': 1.07851912117348, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.49483057988121,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 18.110143078867456, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p2': {'value': 0.5687451525358826,\n", + " 'value_error': 0.04553605563058178},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.73470981177333,\n", + " 'value_error': 15.068397750206863},\n", + " 'HadISST': {'value': 25.80734530798602,\n", + " 'value_error': 11.977879022260755},\n", + " 'Tropflux': {'value': 37.08424857042152,\n", + " 'value_error': 14.985145321762225}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p2': {'value': 1.8054063480269347,\n", + " 'value_error': 0.2895619202146194},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 11.788740112764007,\n", + " 'value_error': 42.22038300850839},\n", + " 'HadISST': {'value': 8.498851717806527,\n", + " 'value_error': 35.090103372123735},\n", + " 'Tropflux': {'value': 12.069224016780282,\n", + " 'value_error': 42.08613554542458}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': 13.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 60.150375939849624,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 72.95918367346938, 'value_error': None},\n", + " 'Tropflux': {'value': 58.59375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12486141501486193,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13925747087749413, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12385770005884607, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p2': {'value': -0.17271086765570529,\n", + " 'value_error': -0.013827936189891412},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 144.1737114061306,\n", + " 'value_error': -10.521204464407804},\n", + " 'HadISST': {'value': 144.29630011299855,\n", + " 'value_error': -7.15132415856745},\n", + " 'Tropflux': {'value': 143.4550239984356,\n", + " 'value_error': -10.350028963829306}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18549652677718456,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1779598123097036, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18677890914928053, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7226526539245978,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8877431631856268, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25561494467359275,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4696760846888414, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28669404633423856,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2878635729263706, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2947199996227253, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.003389673466228,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1360017662349744, 'value_error': None}}}}},\n", + " 'r1i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r1i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r1i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.128263167448335,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.880944762216224, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7797321355068332,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3275031396047816, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1454306746231726,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3253787324684903, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1089049142213498, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.93265337868443,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.54119660973642, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r1i1p3': {'value': 0.5320974840963134,\n", + " 'value_error': 0.042601893886337186},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 40.811272694487286,\n", + " 'value_error': 14.097450319353303},\n", + " 'HadISST': {'value': 30.588023961124666,\n", + " 'value_error': 11.206072287627727},\n", + " 'Tropflux': {'value': 41.13828857012433,\n", + " 'value_error': 14.019562345235576}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r1i1p3': {'value': 1.627933337808474,\n", + " 'value_error': 0.26109773225976485},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 20.459983483797714,\n", + " 'value_error': 38.07008273218271},\n", + " 'HadISST': {'value': 2.16667952985377,\n", + " 'value_error': 31.64071576964098},\n", + " 'Tropflux': {'value': 20.712895576726698,\n", + " 'value_error': 37.94903191118099}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': 27.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.293233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 43.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 14.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12365398554477006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12908578600863693, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12198065825248448, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r1i1p3': {'value': -0.05973828808875964,\n", + " 'value_error': -0.004782890891564754},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 115.27907267068834,\n", + " 'value_error': -3.63913835803589},\n", + " 'HadISST': {'value': 115.3214744001609,\n", + " 'value_error': -2.4735436084557056},\n", + " 'Tropflux': {'value': 115.03048868758756,\n", + " 'value_error': -3.5799311320744023}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16733127422565822,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14661225577017525, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16568591989902487, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.744855102654373,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8919231482468437, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22664251980699532,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5031881039156952, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.267940988156236,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2712087178953018, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27531302584686135, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7938017985707055,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.8707424427332244, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0592715886412196,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8571885844530134, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6878915086203705,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1580626177006734, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9996179994270462,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1702613681375904, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9665275062837124, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.049267673448096,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.666200090682857, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r2i1p1': {'value': 0.534485651423747,\n", + " 'value_error': 0.04279310029889698},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 40.545621025520134,\n", + " 'value_error': 14.16072269191484},\n", + " 'HadISST': {'value': 30.27648816502862,\n", + " 'value_error': 11.256367537099962},\n", + " 'Tropflux': {'value': 40.874104618357684,\n", + " 'value_error': 14.082485139908568}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r2i1p1': {'value': 1.6388715207549633,\n", + " 'value_error': 0.2628520637769358},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 19.92554928307532,\n", + " 'value_error': 38.32587792971407},\n", + " 'HadISST': {'value': 1.5093315090769601,\n", + " 'value_error': 31.853311660153377},\n", + " 'Tropflux': {'value': 20.18016070771377,\n", + " 'value_error': 38.20401376089571}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': 21.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 36.84210526315789,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 57.14285714285714, 'value_error': None},\n", + " 'Tropflux': {'value': 34.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.131325684840543,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14133005262288553, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1300643802781607, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r2i1p1': {'value': 0.019284752800027467,\n", + " 'value_error': 0.0015440159312279415},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 95.06759987781903,\n", + " 'value_error': 1.1747889985656477},\n", + " 'HadISST': {'value': 95.05391172408491,\n", + " 'value_error': 0.7985109475899388},\n", + " 'Tropflux': {'value': 95.14784792002978,\n", + " 'value_error': 1.155675683585038}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15568783469423356,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13988583350324485, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15692895036918525, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6779444563053812,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8381369719239615, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.27387073630504616,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5222660099624423, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2802289277332244,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28218297364272393, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28792550579747095, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8815435180645836,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.011969176708827, 'value_error': None}}}}},\n", + " 'r2i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.168353221131648,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.955835209960119, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7886595991390934,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2815051413212557, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1180035753063626,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2974144348597738, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0816567629292606, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.439260468232813,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 18.052408899051283, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r2i1p2': {'value': 0.5514753279029654,\n", + " 'value_error': 0.04415336306307845},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.655746785943165,\n", + " 'value_error': 14.610849082785565},\n", + " 'HadISST': {'value': 28.060189362777027,\n", + " 'value_error': 11.614173293488516},\n", + " 'Tropflux': {'value': 38.994671875116815,\n", + " 'value_error': 14.530124596483542}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r2i1p2': {'value': 1.8475278706077511,\n", + " 'value_error': 0.29631762314775356},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 9.730703383645867,\n", + " 'value_error': 43.20541710801023},\n", + " 'HadISST': {'value': 11.030213611830435,\n", + " 'value_error': 35.90878254823686},\n", + " 'Tropflux': {'value': 10.017731193475637,\n", + " 'value_error': 43.06803754806972}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': 15.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 53.383458646616546,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 68.36734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 51.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10446632070903535,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11573034473379536, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10258367277403944, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r2i1p2': {'value': -0.31379188456046064,\n", + " 'value_error': -0.025123457576842794},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 180.25755610117068,\n", + " 'value_error': -19.115580979616464},\n", + " 'HadISST': {'value': 180.48028291550537,\n", + " 'value_error': -12.992972100013084},\n", + " 'Tropflux': {'value': 178.95179996010347,\n", + " 'value_error': -18.80457864579586}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13244276596196589,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11588013655938087, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1319936257702435, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6927833102128855,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8694028357316457, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23122220949049835,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5103354149229604, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2773950797112928,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2783260649964234, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28542586124340935, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8539556913949733,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.981399924267915, 'value_error': None}}}}},\n", + " 'r2i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r2i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r2i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.193984171444032,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9292153556842773, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8321571618371053,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3679297107808575, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1536919611141023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3301034583257407, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1181590487068507, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.933303733050494,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.544617285702696, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r2i1p3': {'value': 0.5966388395324761,\n", + " 'value_error': 0.047769337931372516},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 33.63191026369681,\n", + " 'value_error': 15.807416216580386},\n", + " 'HadISST': {'value': 22.168621218298394,\n", + " 'value_error': 12.565325274488682},\n", + " 'Tropflux': {'value': 33.99859189324849,\n", + " 'value_error': 15.720080734116939}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r2i1p3': {'value': 1.8574137783180669,\n", + " 'value_error': 0.297903184438591},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 9.24768282973626,\n", + " 'value_error': 43.43660429219868},\n", + " 'HadISST': {'value': 11.624323428675646,\n", + " 'value_error': 36.10092628577349},\n", + " 'Tropflux': {'value': 9.536246492141471,\n", + " 'value_error': 43.29848963014011}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': 22.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.08163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09224817205889824,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09504565490348138, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08915313037877019, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r2i1p3': {'value': -0.08441506322148921,\n", + " 'value_error': -0.006758614113498389},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 121.59057326091289,\n", + " 'value_error': -5.1423986925924074},\n", + " 'HadISST': {'value': 121.65049035577185,\n", + " 'value_error': -3.4953184426486357},\n", + " 'Tropflux': {'value': 121.23930386032136,\n", + " 'value_error': -5.058734063380372}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20633674607208682,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20781333142521483, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21001422855671462, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7550012000106794,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9054659787078168, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19449383950476198,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4878578952425641, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2686619664012364,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2721274255856839, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2760195650702864, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.731823245981631,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7805763863906061, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0244041948968987,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8245510417703894, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6220952060995353,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0974903650183356, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9912860912166486,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1641041337713631, 'value_error': None},\n", + " 'Tropflux': {'value': 0.95774685760522, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.882582615548607,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.49830388119988, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r3i1p1': {'value': 0.5159839753280187,\n", + " 'value_error': 0.04131178068114263},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 42.603684996607754,\n", + " 'value_error': 13.670537213914935},\n", + " 'HadISST': {'value': 32.69002691726838,\n", + " 'value_error': 10.866718786696401},\n", + " 'Tropflux': {'value': 42.92079785007886,\n", + " 'value_error': 13.595007921413643}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r3i1p1': {'value': 1.7340110806454694,\n", + " 'value_error': 0.27811111816121664},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 15.277077512589734,\n", + " 'value_error': 40.55076689292611},\n", + " 'HadISST': {'value': 4.208236179957989,\n", + " 'value_error': 33.70245603420784},\n", + " 'Tropflux': {'value': 15.54646960708328,\n", + " 'value_error': 40.42182828096748}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': 21.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 36.09022556390977,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 56.63265306122449, 'value_error': None},\n", + " 'Tropflux': {'value': 33.59375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12469552478549827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13460562359791128, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12330886136215513, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r3i1p1': {'value': -0.15546579742050537,\n", + " 'value_error': -0.012447225560390601},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 139.7629944310601,\n", + " 'value_error': -9.470668893540857},\n", + " 'HadISST': {'value': 139.87334273355353,\n", + " 'value_error': -6.437268991899982},\n", + " 'Tropflux': {'value': 139.11606750370478,\n", + " 'value_error': -9.3165851577719}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21102546989844995,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19293695842670064, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20958073219155607, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.687848530273943,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.855704645250671, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2990622941687365,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5116448912515735, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28116055440192705,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28303076291697227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2888871255385333, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9681409045311822,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.109646693245939, 'value_error': None}}}}},\n", + " 'r3i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1475898726649905,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.934752290513076, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.751458702890003,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2214212762455947, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0871020235114779,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.265811608330247, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0513814006661315, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.97996620519939,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.595007961117314, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r3i1p2': {'value': 0.5713430352617417,\n", + " 'value_error': 0.045744052712928744},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.4457301100675,\n", + " 'value_error': 15.137226346014836},\n", + " 'HadISST': {'value': 25.468452193643653,\n", + " 'value_error': 12.03259091715009},\n", + " 'Tropflux': {'value': 36.79686547257079,\n", + " 'value_error': 15.053593641721205}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r3i1p2': {'value': 1.7440102252352971,\n", + " 'value_error': 0.2797148410633203},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.788524260837132,\n", + " 'value_error': 40.78460218147561},\n", + " 'HadISST': {'value': 4.809151152558077,\n", + " 'value_error': 33.89680065788401},\n", + " 'Tropflux': {'value': 15.059469800135192,\n", + " 'value_error': 40.65492004726469}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 54.88721804511278,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.38775510204081, 'value_error': None},\n", + " 'Tropflux': {'value': 53.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08890059905814722,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10056336690436711, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08643010495323723, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r3i1p2': {'value': -0.04116110371167336,\n", + " 'value_error': -0.003295525772964987},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 110.5276450821953,\n", + " 'value_error': -2.5074530283439547},\n", + " 'HadISST': {'value': 110.55686088399031,\n", + " 'value_error': -1.7043304764896559},\n", + " 'Tropflux': {'value': 110.35636479551769,\n", + " 'value_error': -2.466657839090116}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16160619761453474,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15494591283252934, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1634705629062798, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.695994405999954,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8786159676057957, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2509432779816907,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4980579337247631, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2752350954566879,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2760413062224754, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2833233255934895, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9779928770517161,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1243224173398723, 'value_error': None}}}}},\n", + " 'r3i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r3i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r3i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.199004345997784,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9421091615948822, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8868041518578165,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4096178125393735, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1656018452387413,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3412242135013825, 'value_error': None},\n", + " 'Tropflux': {'value': 1.129997501928908, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.38085808060075,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.993232170205992, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r3i1p3': {'value': 0.6188842567485312,\n", + " 'value_error': 0.049550396726087916},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 31.157405172520473,\n", + " 'value_error': 16.396788790986054},\n", + " 'HadISST': {'value': 19.266712427284247,\n", + " 'value_error': 13.033817911349994},\n", + " 'Tropflux': {'value': 31.537758365662004,\n", + " 'value_error': 16.306197043397987}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r3i1p3': {'value': 1.75502581945899,\n", + " 'value_error': 0.28148158826635494},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.250307783454938,\n", + " 'value_error': 41.04220768269638},\n", + " 'HadISST': {'value': 5.47115133083714,\n", + " 'value_error': 34.110901123652944},\n", + " 'Tropflux': {'value': 14.522964680905856,\n", + " 'value_error': 40.91170644447571}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': 24.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 27.819548872180448,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 51.02040816326531, 'value_error': None},\n", + " 'Tropflux': {'value': 25.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.06915501242109422,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07180549944321282, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06460129514700805, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r3i1p3': {'value': -0.24545398973368115,\n", + " 'value_error': -0.019652047110073588},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 162.77898926194644,\n", + " 'value_error': -14.952571587682625},\n", + " 'HadISST': {'value': 162.95321041899032,\n", + " 'value_error': -10.163350288404715},\n", + " 'Tropflux': {'value': 161.75760193418574,\n", + " 'value_error': -14.709299637677672}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17508961495507536,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1647664387258795, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17554458536550868, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.761454396173159,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8972311990280781, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19407181121690856,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.47015884408441927, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2701624043369671,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27275368306369824, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27778231122989716, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7675586779114383,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.802783969360823, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0515470914496596,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.84548882491522, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6700550282475934,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.146754740051453, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9841903361742905,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1548786005268206, 'value_error': None},\n", + " 'Tropflux': {'value': 0.951206754576674, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.86336392580538,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.478975333417328, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r4i1p1': {'value': 0.5421959299828211,\n", + " 'value_error': 0.043410416634390714},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 39.68795567523637,\n", + " 'value_error': 14.364999675331747},\n", + " 'HadISST': {'value': 29.270684366680594,\n", + " 'value_error': 11.418747442048147},\n", + " 'Tropflux': {'value': 40.02117784243249,\n", + " 'value_error': 14.285633499351865}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r4i1p1': {'value': 1.676177049192131,\n", + " 'value_error': 0.2688353486261848},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 18.102819642294982,\n", + " 'value_error': 39.198287457172164},\n", + " 'HadISST': {'value': 0.7326053283459079,\n", + " 'value_error': 32.57838657231515},\n", + " 'Tropflux': {'value': 18.36322677063686,\n", + " 'value_error': 39.07364930201153}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': 22.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 54.08163265306123, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14756912568335323,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1587223422059059, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14683736346959242, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r4i1p1': {'value': -0.07953217609864423,\n", + " 'value_error': -0.006367670263388489},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 120.34169269235915,\n", + " 'value_error': -4.844942866601838},\n", + " 'HadISST': {'value': 120.39814395541322,\n", + " 'value_error': -3.2931359794421535},\n", + " 'Tropflux': {'value': 120.01074204493017,\n", + " 'value_error': -4.766117716565989}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1598785501678474,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14584622320061733, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16071237634519756, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.676842263680256,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8393227769509316, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31741883461133935,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5434215283310725, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28317581260020025,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28559711688298833, 'value_error': None},\n", + " 'Tropflux': {'value': 0.290734845376643, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.941092256260677,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0677911087162153, 'value_error': None}}}}},\n", + " 'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1740319323509865,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9621924995839386, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.764447698074173,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.270993734223533, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1138058621535214,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.294140415788202, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0774968532602138, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.170061541011787,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.782273905290737, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r4i1p2': {'value': 0.517365259084663,\n", + " 'value_error': 0.04142237189005884},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 42.45003565593777,\n", + " 'value_error': 13.70713310041738},\n", + " 'HadISST': {'value': 32.50983881662002,\n", + " 'value_error': 10.895808880315188},\n", + " 'Tropflux': {'value': 42.7679974172325,\n", + " 'value_error': 13.631401616782501}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r4i1p2': {'value': 1.7499157524368758,\n", + " 'value_error': 0.2806620050069043},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.499983126970562,\n", + " 'value_error': 40.92270606074374},\n", + " 'HadISST': {'value': 5.1640534829170015,\n", + " 'value_error': 34.011581222490285},\n", + " 'Tropflux': {'value': 14.77184613580482,\n", + " 'value_error': 40.792584799880885}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': 24.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 27.819548872180448,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 51.02040816326531, 'value_error': None},\n", + " 'Tropflux': {'value': 25.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14228211000810323,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15108262900914077, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14109364134571675, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r4i1p2': {'value': -0.276848613535744,\n", + " 'value_error': -0.022165628684488372},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 170.80869272160913,\n", + " 'value_error': -16.86506997639508},\n", + " 'HadISST': {'value': 171.00519751596403,\n", + " 'value_error': -11.46328661952425},\n", + " 'Tropflux': {'value': 169.65666554991662,\n", + " 'value_error': -16.59068249487937}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1398282610352986,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14275201070086227, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14296586604552647, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7089014167122109,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8898968503897121, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.261328471087466,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.524779071835963, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.27637783105961483,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2773899865018505, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28435465574385005, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9081130199726781,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0350597266478987, 'value_error': None}}}}},\n", + " 'r4i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r4i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r4i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.196903694526837,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.935410526284118, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8638999763669404,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3942753173134577, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1659858593734822,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3430512230929665, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1300397761664573, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.130691800878708,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.74117032546684, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r4i1p3': {'value': 0.5752187447963482,\n", + " 'value_error': 0.0460543578191594},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 36.01460926921482,\n", + " 'value_error': 15.239909828364226},\n", + " 'HadISST': {'value': 24.962866910137503,\n", + " 'value_error': 12.114214083033705},\n", + " 'Tropflux': {'value': 36.36812656094172,\n", + " 'value_error': 15.155709801027461}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r4i1p3': {'value': 1.8615322583213392,\n", + " 'value_error': 0.2985637310127272},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 9.04645593679547,\n", + " 'value_error': 43.532917126891576},\n", + " 'HadISST': {'value': 11.871830230491282,\n", + " 'value_error': 36.18097357773566},\n", + " 'Tropflux': {'value': 9.33565943706942,\n", + " 'value_error': 43.39449622048375}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': 17.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 48.87218045112782,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 65.3061224489796, 'value_error': None},\n", + " 'Tropflux': {'value': 46.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09417170338323333,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10449103795650028, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09175263780437039, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r4i1p3': {'value': -0.12886790947730895,\n", + " 'value_error': -0.010317690214661286},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 132.96013690412354,\n", + " 'value_error': -7.8503781662101755},\n", + " 'HadISST': {'value': 133.0516062516755,\n", + " 'value_error': -5.33594791583296},\n", + " 'Tropflux': {'value': 132.4238895616462,\n", + " 'value_error': -7.722655868171252}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24131544732441912,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22525480861176733, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2402994064134987, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7501034861749311,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8886123752967068, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19376167847796702,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4707488233953825, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2748203794731039,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.277662572064297, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2823925669713831, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7827024988769753,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.8176483130182801, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0342431129161938,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.841692891304727, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.648391355081595,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.129319090507731, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9960408525406175,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1691586864344647, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9622495449759947, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.06158267233974,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.67638363846943, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r5i1p1': {'value': 0.50952389658486,\n", + " 'value_error': 0.04079456043985388},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 43.322282341134475,\n", + " 'value_error': 13.499383164398129},\n", + " 'HadISST': {'value': 33.5327424803591,\n", + " 'value_error': 10.730668323119128},\n", + " 'Tropflux': {'value': 43.6354249666492,\n", + " 'value_error': 13.424799492691864}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r5i1p1': {'value': 1.7327751750335678,\n", + " 'value_error': 0.27791289619164317},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 15.337463248602399,\n", + " 'value_error': 40.521864586055486},\n", + " 'HadISST': {'value': 4.133962407812755,\n", + " 'value_error': 33.67843481830416},\n", + " 'Tropflux': {'value': 15.606663335556256,\n", + " 'value_error': 40.39301787429051}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': 27.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.796992481203006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 44.89795918367347, 'value_error': None},\n", + " 'Tropflux': {'value': 15.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12685798163740106,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13617834351646066, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1254359930783098, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r5i1p1': {'value': 0.006673935599270866,\n", + " 'value_error': 0.0005343424930626458},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 98.29302863735828,\n", + " 'value_error': 0.4065629567804403},\n", + " 'HadISST': {'value': 98.28829153455786,\n", + " 'value_error': 0.27634321760766445},\n", + " 'Tropflux': {'value': 98.32080033198334,\n", + " 'value_error': 0.39994835121137123}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1364200794698191,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1284768259544277, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13863446780615277, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6786290564241176,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8514846544238807, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31648432694831635,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5564187005914384, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.281842100447578,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2838804914772946, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28953034519874205, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.951022909675836,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0750878570644904, 'value_error': None}}}}},\n", + " 'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1590673586330986,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.953065835230972, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7574115403263306,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.229012288499285, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1014626085775572,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2808796610485396, 'value_error': None},\n", + " 'Tropflux': {'value': 1.065323646666712, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.253444106143686,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.86420293192252, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r5i1p2': {'value': 0.547611249865604,\n", + " 'value_error': 0.043843989221937725},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 39.08557377832038,\n", + " 'value_error': 14.508473766626494},\n", + " 'HadISST': {'value': 28.564257320544556,\n", + " 'value_error': 11.532795089106953},\n", + " 'Tropflux': {'value': 39.42212408674297,\n", + " 'value_error': 14.428314900758993}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r5i1p2': {'value': 1.710263863217174,\n", + " 'value_error': 0.2743023967142105},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 16.437354793355563,\n", + " 'value_error': 39.99542564451149},\n", + " 'HadISST': {'value': 2.781108250957993,\n", + " 'value_error': 33.24090214897317},\n", + " 'Tropflux': {'value': 16.70305757310781,\n", + " 'value_error': 39.868252842060805}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': 17.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 48.1203007518797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 64.79591836734694, 'value_error': None},\n", + " 'Tropflux': {'value': 46.09375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14915684387404682,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16006714792164697, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14835090006234922, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r5i1p2': {'value': -0.21588756760843214,\n", + " 'value_error': -0.017284838815304585},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 155.2168791527194,\n", + " 'value_error': -13.151443629244863},\n", + " 'HadISST': {'value': 155.37011431447388,\n", + " 'value_error': -8.939113089572531},\n", + " 'Tropflux': {'value': 154.3185241248969,\n", + " 'value_error': -12.937475261442346}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14906173015760055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14086237573210225, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1520189931216918, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6943984416290795,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8875512492106586, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22775106326816255,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.526490311061547, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2741137824061624,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2756548609875701, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2819414288146932, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9450859480477363,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.091738226686371, 'value_error': None}}}}},\n", + " 'r5i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r5i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r5i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1579910538962386,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9050670956129165, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8070727424884052,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3512994769219295, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.151604733537532,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3302880550861356, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1152763312184872, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.04103552528316,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.649781113386517, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r5i1p3': {'value': 0.5358048162257011,\n", + " 'value_error': 0.042898718011047755},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 40.39888158760211,\n", + " 'value_error': 14.195672791876635},\n", + " 'HadISST': {'value': 30.104403465586614,\n", + " 'value_error': 11.284149393943538},\n", + " 'Tropflux': {'value': 40.72817591126571,\n", + " 'value_error': 14.117242141656178}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r5i1p3': {'value': 1.735063056193777,\n", + " 'value_error': 0.27827984032180925},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 15.225678508377637,\n", + " 'value_error': 40.57536789790965},\n", + " 'HadISST': {'value': 4.27145637365639,\n", + " 'value_error': 33.722902362412796},\n", + " 'Tropflux': {'value': 15.495234035446373,\n", + " 'value_error': 40.44635106253681}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': 19.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.224489795918366, 'value_error': None},\n", + " 'Tropflux': {'value': 40.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.08249862809988076,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08985024387178614, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0792876253237246, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r5i1p3': {'value': 0.014653362756465718,\n", + " 'value_error': 0.001173207962614539},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 96.25215583524354,\n", + " 'value_error': 0.8926538772258942},\n", + " 'HadISST': {'value': 96.24175500282365,\n", + " 'value_error': 0.6067420568661835},\n", + " 'Tropflux': {'value': 96.31313165822681,\n", + " 'value_error': 0.8781307801038523}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16796534276098346,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1598922371433367, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16925646390187538, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.723016489292259,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.874471391425433, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20872904123300837,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.47615833595162976, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26918608233389263,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.271990171649261, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2767736268089055, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7297991206381993,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.7869111528564348, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0682370623065602,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.863735583165499, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6972243081315266,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.17149361382077, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0091961058702132,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1803929125999657, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9756694672993207, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.101090570860677,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.720235432292785, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r6i1p1': {'value': 0.5294391671320865,\n", + " 'value_error': 0.04238905819248199},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 41.10697489679043,\n", + " 'value_error': 14.027020572067558},\n", + " 'HadISST': {'value': 30.9348007058748,\n", + " 'value_error': 11.15008763640325},\n", + " 'Tropflux': {'value': 41.43235702696587,\n", + " 'value_error': 13.949521720110889}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r6i1p1': {'value': 1.7472456641012186,\n", + " 'value_error': 0.280233760192954},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 14.630442320467882,\n", + " 'value_error': 40.86026463179829},\n", + " 'HadISST': {'value': 5.003590150814222,\n", + " 'value_error': 33.95968505196153},\n", + " 'Tropflux': {'value': 14.90189051033284,\n", + " 'value_error': 40.730341914928395}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': 24.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 50.0, 'value_error': None},\n", + " 'Tropflux': {'value': 23.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10552538441764521,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1168235989365986, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10363762004647109, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r6i1p1': {'value': -0.011750174886691986,\n", + " 'value_error': -0.0009407669057464432},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 103.00530500171539,\n", + " 'value_error': -0.7157974142187847},\n", + " 'HadISST': {'value': 103.01364517604479,\n", + " 'value_error': -0.4865316854414922},\n", + " 'Tropflux': {'value': 102.95640997360336,\n", + " 'value_error': -0.7041516961732696}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09832595693447961,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09419875738703808, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0994212396922834, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6925050583712082,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8687348700833031, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2776320492068834,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5234473719688465, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2837033313035686,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2853203753432155, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29154330558559705, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8663641542590788,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.9770402907565814, 'value_error': None}}}}},\n", + " 'r6i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p2': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1810714610291004,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.975004303963453, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8323236885034087,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.334262774799289, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1266044918239664,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3058222235009318, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0901900752162637, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.594436898331526,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 18.209255285571107, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r6i1p2': {'value': 0.5486273969115458,\n", + " 'value_error': 0.0439253460971683},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.97254101232883,\n", + " 'value_error': 14.535395680233453},\n", + " 'HadISST': {'value': 28.431701207213795,\n", + " 'value_error': 11.554195335475384},\n", + " 'Tropflux': {'value': 39.309715823264476,\n", + " 'value_error': 14.455088071631437}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r6i1p2': {'value': 1.711770470080249,\n", + " 'value_error': 0.27454403537730543},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 16.363742728297836,\n", + " 'value_error': 40.03065844341673},\n", + " 'HadISST': {'value': 2.87165025819777,\n", + " 'value_error': 33.27018474821971},\n", + " 'Tropflux': {'value': 16.629679571132467,\n", + " 'value_error': 39.90337361181014}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': 21.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 35.338345864661655,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 56.12244897959183, 'value_error': None},\n", + " 'Tropflux': {'value': 32.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09530449269928601,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1059809242816384, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09305570590976153, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r6i1p2': {'value': -0.07866120250526179,\n", + " 'value_error': -0.006297936566627833},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 120.1189265359576,\n", + " 'value_error': -4.791884877932911},\n", + " 'HadISST': {'value': 120.17475958935306,\n", + " 'value_error': -3.2570721544821235},\n", + " 'Tropflux': {'value': 119.79160019869796,\n", + " 'value_error': -4.713922958699281}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12569047615710846,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12467460797363646, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12789041734950604, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7085713772374419,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8926147629564607, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23766171036982797,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5142811976378425, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.27974283869322786,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.28093344415434707, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2877231661380612, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9562498918057885,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0785980493043095, 'value_error': None}}}}},\n", + " 'r6i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R_r6i1p3': {'keyerror': None,\n", + " 'name': 'GISS-E2-R_r6i1p3',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0987849162753642,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8586486917491043, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7638051749233594,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.2937331567414048, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1315757843690353,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3106394023794659, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0952986480036917, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.86212380827721,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.474665499458062, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R_r6i1p3': {'value': 0.5536259013097853,\n", + " 'value_error': 0.044325546737706646},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 38.41652426238072,\n", + " 'value_error': 14.66782661541242},\n", + " 'HadISST': {'value': 27.77964763077176,\n", + " 'value_error': 11.659464770665116},\n", + " 'Tropflux': {'value': 38.75677104854932,\n", + " 'value_error': 14.586787330016515}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R_r6i1p3': {'value': 1.6519249883150824,\n", + " 'value_error': 0.264945657352863},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 19.287763323901277,\n", + " 'value_error': 38.63114017750647},\n", + " 'HadISST': {'value': 0.7248619946342066,\n", + " 'value_error': 32.10702048672853},\n", + " 'Tropflux': {'value': 19.544402706149498,\n", + " 'value_error': 38.508305371298746}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': 20.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 39.849624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 59.183673469387756, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10192088217861611,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10963579834004553, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09967695742342964, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R_r6i1p3': {'value': -0.018970603196765388,\n", + " 'value_error': -0.0015188638332334628},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 104.85205107350089,\n", + " 'value_error': -1.1556516260702356},\n", + " 'HadISST': {'value': 104.86551624651497,\n", + " 'value_error': -0.7855031636692946},\n", + " 'Tropflux': {'value': 104.77311027597635,\n", + " 'value_error': -1.1368496679621003}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14661537337373867,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12809285505801982, 'value_error': None},\n", + " 'Tropflux': {'value': 0.147454980660331, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7371095651469477,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8987591310375709, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2070078092022702,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.515191344263839, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26457151425271597,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2677541592147845, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2719434705803146, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8123892291483008,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.8812463468623393, 'value_error': None}}}}}},\n", + " 'GISS-E2-R-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GISS-E2-R-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'GISS-E2-R-CC_r1i1p1',\n", + " 'nyears': 161,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"GISS-E2-R-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.079758256576057,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8764126979747977, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7128748534447555,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1900842139554513, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0217803569263653,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.1948350235189096, 'value_error': None},\n", + " 'Tropflux': {'value': 0.987734107060135, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.291301210134197,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 17.9060524564541, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': 0.5290936111877899,\n", + " 'value_error': 0.04169841808517218},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 41.145413373135625,\n", + " 'value_error': 13.94411844198214},\n", + " 'HadISST': {'value': 30.979878387394656,\n", + " 'value_error': 11.056325447410265},\n", + " 'Tropflux': {'value': 41.470583131925956,\n", + " 'value_error': 13.867077621713191}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': 12.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': 1.7089041553671014,\n", + " 'value_error': 0.26978125065816283},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 16.50378944540193,\n", + " 'value_error': 39.753382388703656},\n", + " 'HadISST': {'value': 2.6993943805233407,\n", + " 'value_error': 32.955874781103454},\n", + " 'Tropflux': {'value': 16.76928098397486,\n", + " 'value_error': 39.62697920724485}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': 25.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 24.81203007518797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 48.97959183673469, 'value_error': None},\n", + " 'Tropflux': {'value': 21.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14438370609109066,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15152841520028515, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14309581712757302, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': -0.4467820947253688,\n", + " 'value_error': -0.03521136181743668},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 214.27204079113426,\n", + " 'value_error': -27.073894537749876},\n", + " 'HadISST': {'value': 214.5891629270344,\n", + " 'value_error': -18.356025003529215},\n", + " 'Tropflux': {'value': 212.41288351967165,\n", + " 'value_error': -26.6334138431881}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18839722997106523,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17551758333459933, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19021383922729804, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6942812244180467,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8763603476338755, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25363498934554923,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5387565761096764, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2809047142669015,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2827717890889462, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2886241745579582, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8871955703371914,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.001606989256877, 'value_error': None}}}}}},\n", + " 'HadCM3': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r10i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r10i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5851853707205381,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.291585084224255, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.869544721482936,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.114314499972585, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.131979593245782,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0295139701596052, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1698790354058544, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.93384903157396,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.961167688445412, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r10i1p1': {'value': 0.8460254620693382,\n", + " 'value_error': 0.07001756543161235},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 5.890984519523699,\n", + " 'value_error': 22.66845940193888},\n", + " 'HadISST': {'value': 10.363797718701143,\n", + " 'value_error': 18.11505137301515},\n", + " 'Tropflux': {'value': 6.410933900152167,\n", + " 'value_error': 22.543216869481558}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r10i1p1': {'value': 1.2554795318866563,\n", + " 'value_error': 0.20816612553299027},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 38.65789195247308,\n", + " 'value_error': 29.69253118946757},\n", + " 'HadISST': {'value': 24.54990107142392,\n", + " 'value_error': 24.810601040133676},\n", + " 'Tropflux': {'value': 38.85294045271843,\n", + " 'value_error': 29.598118332437828}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': 41.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.308270676691727,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 16.3265306122449, 'value_error': None},\n", + " 'Tropflux': {'value': 28.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25809216456470957,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2695305415549711, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25735288040529986, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r10i1p1': {'value': 0.4053168408152692,\n", + " 'value_error': 0.03354426042084254},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 3.666604176351789,\n", + " 'value_error': 24.97063853139572},\n", + " 'HadISST': {'value': 3.954294627229498,\n", + " 'value_error': 17.062999158633676},\n", + " 'Tropflux': {'value': 1.979992826559088,\n", + " 'value_error': 24.564376913266727}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09546381093109907,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09057408162025672, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09807732140668304, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.425917500585054,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5321681061491997, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5919658255510504,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4224325953134937, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30139612955442185,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3086219933519705, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3083511093639166, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r10i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.522207977309373,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.885918526678999, 'value_error': None}}}}},\n", + " 'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5976500880109358,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3327565613048185, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7150533871674902,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9871758139345757, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0538010637733595,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9650467581116231, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0900248910639958, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.973757108685511,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.953679279790277, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r1i1p1': {'value': 0.8888898606977632,\n", + " 'value_error': 0.07356504830324073},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1228935637066386,\n", + " 'value_error': 23.816970792743028},\n", + " 'HadISST': {'value': 15.95544718039732,\n", + " 'value_error': 19.03286155490738},\n", + " 'Tropflux': {'value': 1.6691864984207574,\n", + " 'value_error': 23.685382770608186}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r1i1p1': {'value': 1.5448598690424087,\n", + " 'value_error': 0.2561471415999108},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.518912018676353,\n", + " 'value_error': 36.53647764051297},\n", + " 'HadISST': {'value': 7.159115708658363,\n", + " 'value_error': 30.52929251353551},\n", + " 'Tropflux': {'value': 24.758918002758904,\n", + " 'value_error': 36.42030319860255}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': 26.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30075187969925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 17.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.258390752324202,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27194247214468287, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2579433444753308, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r1i1p1': {'value': 0.2263007504437517,\n", + " 'value_error': 0.018728783366238382},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 42.11977406644193,\n", + " 'value_error': 13.941868853384271},\n", + " 'HadISST': {'value': 41.95914771595974,\n", + " 'value_error': 9.526792685576558},\n", + " 'Tropflux': {'value': 43.061460608252794,\n", + " 'value_error': 13.71504060495976}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11902205304828674,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11146145382380687, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12156555963374888, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4246543173169749,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5490032034708046, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5886815254843234,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4082588825315297, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29130592261272664,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29771913762923613, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29852670949891985, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4202471794726916,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.761715448365351, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6217342934124882,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3476742328740268, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7471550090403154,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.018477199642942, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0623412739902156,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9751132272041184, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0983441693841105, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.73435754003928,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.732295635464192, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r2i1p1': {'value': 0.85744289857408,\n", + " 'value_error': 0.07096247920001915},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 4.62094743796357,\n", + " 'value_error': 22.974378913213283},\n", + " 'HadISST': {'value': 11.85319929036704,\n", + " 'value_error': 18.359520905085308},\n", + " 'Tropflux': {'value': 5.147913733927523,\n", + " 'value_error': 22.847446184980384}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r2i1p1': {'value': 1.3768086738462197,\n", + " 'value_error': 0.22828323358174554},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.72981017465785,\n", + " 'value_error': 32.56200794343102},\n", + " 'HadISST': {'value': 17.258427549739498,\n", + " 'value_error': 27.208289619872495},\n", + " 'Tropflux': {'value': 32.9437080998235,\n", + " 'value_error': 32.45847105797833}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': 29.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.781954887218044,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 40.816326530612244, 'value_error': None},\n", + " 'Tropflux': {'value': 9.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25597087452251654,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26993796526746444, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2555165234986296, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r2i1p1': {'value': 0.4917605555590046,\n", + " 'value_error': 0.040698393156300416},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 25.776039209564676,\n", + " 'value_error': 30.29623701833523},\n", + " 'HadISST': {'value': 26.125086674920606,\n", + " 'value_error': 20.70210043302109},\n", + " 'Tropflux': {'value': 23.729716799871863,\n", + " 'value_error': 29.80333018862748}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09071596931545785,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06454118779436058, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08911194543606218, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.41777234092354026,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5319981677475185, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5820243765955747,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4042062403636551, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29189264603330517,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2977143235588403, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29928331889775417, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.450315907185167,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.7898851349719327, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6176774323043652,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3197332959185548, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.810017525751351,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0780089285014736, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.064311190035183,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9711107507903284, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1011927451622523, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.416207620972541,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.43052360892823, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r3i1p1': {'value': 0.7499899119165737,\n", + " 'value_error': 0.06206960674945276},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 16.573654818708913,\n", + " 'value_error': 20.095276835475666},\n", + " 'HadISST': {'value': 2.164014393406681,\n", + " 'value_error': 16.058743374438006},\n", + " 'Tropflux': {'value': 17.034582778518548,\n", + " 'value_error': 19.98425105658702}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r3i1p1': {'value': 1.3808450075463459,\n", + " 'value_error': 0.2289524822045812},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.53259690939901,\n", + " 'value_error': 32.657468650864466},\n", + " 'HadISST': {'value': 17.01585746456605,\n", + " 'value_error': 27.288055050176396},\n", + " 'Tropflux': {'value': 32.747121910366936,\n", + " 'value_error': 32.55362823055765}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': 27.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 18.796992481203006,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 44.89795918367347, 'value_error': None},\n", + " 'Tropflux': {'value': 15.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2530586135964928,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26728593272680734, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2527399833029206, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r3i1p1': {'value': -0.026528735379019737,\n", + " 'value_error': -0.0021955337616850704},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 106.7851705946989,\n", + " 'value_error': -1.6343743835368272},\n", + " 'HadISST': {'value': 106.8040004657377,\n", + " 'value_error': -1.1168047903988696},\n", + " 'Tropflux': {'value': 106.67477876865017,\n", + " 'value_error': -1.6077838107387241}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1266611450708587,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10639201535332796, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12702187775863794, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4473429384788433,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5432053011822907, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5920146784986211,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4160529241274529, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3008505993669579,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30694246884007964, 'value_error': None},\n", + " 'Tropflux': {'value': 0.30825521196123873, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.491801871834863,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8353448037938986, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6277095507247508,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3390411071232946, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.669473483145014,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9367396653597908, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.025298585343199,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9445503970597269, 'value_error': None},\n", + " 'Tropflux': {'value': 1.060362179772569, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.073673180703343,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.042710309451541, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r4i1p1': {'value': 0.9196165666320956,\n", + " 'value_error': 0.07610800858010142},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2950414442602174,\n", + " 'value_error': 24.640264082668434},\n", + " 'HadISST': {'value': 19.963737841063917,\n", + " 'value_error': 19.690780118212203},\n", + " 'Tropflux': {'value': 1.7298645250378406,\n", + " 'value_error': 24.504127390738386}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r4i1p1': {'value': 1.2928650376867057,\n", + " 'value_error': 0.21436486927658005},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 36.83125465735777,\n", + " 'value_error': 30.576711511653986},\n", + " 'HadISST': {'value': 22.303157863376754,\n", + " 'value_error': 25.549407882882242},\n", + " 'Tropflux': {'value': 37.032111286411286,\n", + " 'value_error': 30.479487240877983}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': 24.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 50.0, 'value_error': None},\n", + " 'Tropflux': {'value': 23.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.25418561635824055,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2673395953244086, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2536446448843113, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r4i1p1': {'value': 0.23965501135546957,\n", + " 'value_error': 0.019833989863085398},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 38.70419706446449,\n", + " 'value_error': 14.764594159862316},\n", + " 'HadISST': {'value': 38.53409197301732,\n", + " 'value_error': 10.088979399167048},\n", + " 'Tropflux': {'value': 39.70145358450891,\n", + " 'value_error': 14.524380522279046}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10403879766391579,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08124545666306959, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10379743503063231, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4124377616566821,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5642470723963676, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5319540496106101,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3563961567834074, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28357844792528314,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2893927631377913, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29095325094707236, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.453314967207502,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.786110313636023, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6119596908307898,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3308557301251862, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.672706320186576,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9440128337943574, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0157203649344633,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9340050785563994, 'value_error': None},\n", + " 'Tropflux': {'value': 1.051003141869254, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.971353568239103,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.941273714624288, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r5i1p1': {'value': 0.7922918115658557,\n", + " 'value_error': 0.06557053687433399},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 11.868134349844395,\n", + " 'value_error': 21.228716593280556},\n", + " 'HadISST': {'value': 3.354257225264207,\n", + " 'value_error': 16.964509358653824},\n", + " 'Tropflux': {'value': 12.355060163747277,\n", + " 'value_error': 21.11142859501748}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r5i1p1': {'value': 1.2339757570140204,\n", + " 'value_error': 0.2046006691588482},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.708555741220536,\n", + " 'value_error': 29.18395937297804},\n", + " 'HadISST': {'value': 25.842205645310578,\n", + " 'value_error': 24.385646617802255},\n", + " 'Tropflux': {'value': 39.90026346295697,\n", + " 'value_error': 29.091163613456526}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': 24.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 27.819548872180448,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 51.02040816326531, 'value_error': None},\n", + " 'Tropflux': {'value': 25.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2575246613614642,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2697310287041272, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25695485573395926, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r5i1p1': {'value': 0.5615311961353503,\n", + " 'value_error': 0.046472652455554715},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 43.62105488966829,\n", + " 'value_error': 34.59464574576807},\n", + " 'HadISST': {'value': 44.01962496307835,\n", + " 'value_error': 23.63929983252522},\n", + " 'Tropflux': {'value': 41.284401700623505,\n", + " 'value_error': 34.031805642916694}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11787020311770871,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07900188716563394, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11643967036887937, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.43340785505107027,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5917017176166451, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.545616840922194,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.36666316250444614, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29311052151067196,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30040817885652027, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3000171910798353, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.177661879110609,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5029939813112225, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r6i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r6i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6265601409527681,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3599926219442313, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7820485816096903,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0532878629887885, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.061354749451538,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9710917934158275, 'value_error': None},\n", + " 'Tropflux': {'value': 1.097739818893833, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.453282647507587,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.476141369128957, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r6i1p1': {'value': 0.8359293112807367,\n", + " 'value_error': 0.06918200204712595},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 7.014046239833778,\n", + " 'value_error': 22.397942503183284},\n", + " 'HadISST': {'value': 9.046757519171782,\n", + " 'value_error': 17.898873139138026},\n", + " 'Tropflux': {'value': 7.527790739421876,\n", + " 'value_error': 22.274194568169577}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r6i1p1': {'value': 1.2700603086849371,\n", + " 'value_error': 0.2105837068127092},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 37.945482420449125,\n", + " 'value_error': 30.037371673803488},\n", + " 'HadISST': {'value': 23.673645406600315,\n", + " 'value_error': 25.09874419723776},\n", + " 'Tropflux': {'value': 38.142796157661465,\n", + " 'value_error': 29.941862333111303}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': 20.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 39.849624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 59.183673469387756, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2663805489021836,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2803977826641542, 'value_error': None},\n", + " 'Tropflux': {'value': 0.26601422749231607, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r6i1p1': {'value': 0.31969303586214026,\n", + " 'value_error': 0.02645798390246753},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 18.233125127505886,\n", + " 'value_error': 19.69555280126248},\n", + " 'HadISST': {'value': 18.00620972610128,\n", + " 'value_error': 13.458414387530787},\n", + " 'Tropflux': {'value': 19.563437240000912,\n", + " 'value_error': 19.375114573744767}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1266131278785893,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1027739697549678, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12832838253592774, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.42679837392242115,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5373154751894941, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5746094673755986,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.39946573218202897, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29526170283254305,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3017883079125728, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3024981419413787, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.383642673280318,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.7393527562960887, 'value_error': None}}}}},\n", + " 'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r7i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r7i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6080231265065513,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3007843543911328, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7788446031067586,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.042052559626932, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0967364065247733,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9989563926770915, 'value_error': None},\n", + " 'Tropflux': {'value': 1.134091708931195, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.827114479285804,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.83249999407528, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r7i1p1': {'value': 0.8178446899176081,\n", + " 'value_error': 0.06768530813379901},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.025718450807657,\n", + " 'value_error': 21.913382021792106},\n", + " 'HadISST': {'value': 6.687623446471677,\n", + " 'value_error': 17.511646205975406},\n", + " 'Tropflux': {'value': 9.528348524059625,\n", + " 'value_error': 21.792311268352226}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r7i1p1': {'value': 1.4223393378661293,\n", + " 'value_error': 0.23583249398882364},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 30.505204483485908,\n", + " 'value_error': 33.63882411378846},\n", + " 'HadISST': {'value': 14.522187716802042,\n", + " 'value_error': 28.10805987609705},\n", + " 'Tropflux': {'value': 30.726175951076357,\n", + " 'value_error': 33.531863293525674}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': 24.75, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 25.563909774436087,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.48979591836735, 'value_error': None},\n", + " 'Tropflux': {'value': 22.65625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24116122154630015,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2547133680064858, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24052697481641336, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r7i1p1': {'value': 0.2979423400283957,\n", + " 'value_error': 0.024657883507146927},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 23.796231686368802,\n", + " 'value_error': 18.35554244694732},\n", + " 'HadISST': {'value': 23.584754744118097,\n", + " 'value_error': 12.54275516161663},\n", + " 'Tropflux': {'value': 25.036034432449906,\n", + " 'value_error': 18.056905615263727}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07975549323219616,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07480864232507385, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08072618512541341, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.41669263447609073,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5329601482270555, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5836315405609687,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4084844732488913, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29745268501078925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30452628513937885, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3044586517339176, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r7i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.262878596492244,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.6158342539207857, 'value_error': None}}}}},\n", + " 'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r8i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r8i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5972689124167574,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.307048413270318, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.7522234829047405,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0190514626879335, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0754087849352811,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9851374105609607, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1118277451619476, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.014219760631196,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 11.018632570403962, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r8i1p1': {'value': 0.8712872511090549,\n", + " 'value_error': 0.0721082459682024},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 3.0809484125863067,\n", + " 'value_error': 23.34532536512928},\n", + " 'HadISST': {'value': 13.659191416158567,\n", + " 'value_error': 18.65595543175284},\n", + " 'Tropflux': {'value': 3.616423155220595,\n", + " 'value_error': 23.216343169298238}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r8i1p1': {'value': 1.4005542018980341,\n", + " 'value_error': 0.2322203862375543},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 31.569615435990077,\n", + " 'value_error': 33.12359800872611},\n", + " 'HadISST': {'value': 15.831401146586298,\n", + " 'value_error': 27.677545237361972},\n", + " 'Tropflux': {'value': 31.787202413439264,\n", + " 'value_error': 33.01827544450438}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 54.88721804511278,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.38775510204081, 'value_error': None},\n", + " 'Tropflux': {'value': 53.125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2595032255323534,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27223561754845915, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25900023167417063, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r8i1p1': {'value': 0.1916863147650231,\n", + " 'value_error': 0.015864072286402013},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 50.97299860820236,\n", + " 'value_error': 11.809353067553108},\n", + " 'HadISST': {'value': 50.836941290152325,\n", + " 'value_error': 8.069596666593682},\n", + " 'Tropflux': {'value': 51.77064696999341,\n", + " 'value_error': 11.617219939669793}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07103369686249157,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.058937049929853116, 'value_error': None},\n", + " 'Tropflux': {'value': 0.06888708827476607, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4070247561802545,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5660973779582076, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.548599998532659,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3786184991988651, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2888472198095912,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.29570178685208637, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29568632029187125, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r8i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.492553385091434,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8496021067810733, 'value_error': None}}}}},\n", + " 'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadCM3_r9i1p1': {'keyerror': None,\n", + " 'name': 'HadCM3_r9i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadCM3_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.623890675818215,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3475988502886533, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6815948768335303,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9552528725182825, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0273582686667113,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9474240598508203, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0624290663895537, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.997194610340438,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.960099324273816, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadCM3_r9i1p1': {'value': 0.843389267413508,\n", + " 'value_error': 0.06979939240954564},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 6.184225910945014,\n", + " 'value_error': 22.59782503665024},\n", + " 'HadISST': {'value': 10.019906823229109,\n", + " 'value_error': 18.05860531582104},\n", + " 'Tropflux': {'value': 6.70255514201538,\n", + " 'value_error': 22.472972756862074}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': 15.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadCM3_r9i1p1': {'value': 1.2834523242060496,\n", + " 'value_error': 0.21280418425842143},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 37.29115517559492,\n", + " 'value_error': 30.354097537070178},\n", + " 'HadISST': {'value': 22.868830297904314,\n", + " 'value_error': 25.36339522959533},\n", + " 'Tropflux': {'value': 37.49054946647326,\n", + " 'value_error': 30.25758110831752}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': 21.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 36.84210526315789,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 57.14285714285714, 'value_error': None},\n", + " 'Tropflux': {'value': 34.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2973399769681441,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3108338258608661, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2970627173268184, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadCM3_r9i1p1': {'value': 0.1645973314574294,\n", + " 'value_error': 0.013622172076240436},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 57.90146204051263,\n", + " 'value_error': 10.140463097434079},\n", + " 'HadISST': {'value': 57.78463225272239,\n", + " 'value_error': 6.929206599267619},\n", + " 'Tropflux': {'value': 58.586387263020754,\n", + " 'value_error': 9.97548209619291}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1409601156688482,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.098826262844605, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13686957668448746, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4063853076529288,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6034157697661248, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5138792019606134,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.36141946937549474, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2886472615246609,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2974483165710086, 'value_error': None},\n", + " 'Tropflux': {'value': 0.29479609492439046, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadCM3_r9i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9650351396413004,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.311471452440372, 'value_error': None}}}}}},\n", + " 'HadGEM2-AO': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-AO_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-AO_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-AO_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9831010270857359,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7666393072775088, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.219101953008681,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.74835558314193, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8061152124738303,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6200150508385294, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8541196711989075, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.922947301302221,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.999029773635938, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-AO_r1i1p1': {'value': 0.7080523973214107,\n", + " 'value_error': 0.0585988333462223},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 21.23864232463511,\n", + " 'value_error': 18.971600433711817},\n", + " 'HadISST': {'value': 7.634752078162461,\n", + " 'value_error': 15.160779583265857},\n", + " 'Tropflux': {'value': 21.673796373709813,\n", + " 'value_error': 18.86678293195944}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-AO_r1i1p1': {'value': 1.3614892642689074,\n", + " 'value_error': 0.22574318250471212},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 33.47830893839608,\n", + " 'value_error': 32.19969853485388},\n", + " 'HadISST': {'value': 18.179072561290305,\n", + " 'value_error': 26.905549710905657},\n", + " 'Tropflux': {'value': 33.68982687425342,\n", + " 'value_error': 32.09731367871704}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': 5.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 84.9624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 89.79591836734694, 'value_error': None},\n", + " 'Tropflux': {'value': 84.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17868547929839443,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18874883337831602, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17821596072827492, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-AO_r1i1p1': {'value': -0.4568441258391742,\n", + " 'value_error': -0.037808688871790175},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 216.8455746087444,\n", + " 'value_error': -28.145116074070923},\n", + " 'HadISST': {'value': 217.16983868886294,\n", + " 'value_error': -19.232191090656762},\n", + " 'Tropflux': {'value': 214.9445470418251,\n", + " 'value_error': -27.68720706288148}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1865901606577584,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15150613114470107, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1831811644967847, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.646638024013878,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9604794271502959, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7740436604765183,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.769365229904841, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17072542032418442,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18940528305318413, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17021175588685633, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.77094272706078,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.467867902617733, 'value_error': None}}}}}},\n", + " 'HadGEM2-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8563568307567402,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6287637901788639, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0419224469502477,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6459090949252458, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2099022929753394,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0043405921386646, 'value_error': None},\n", + " 'Tropflux': {'value': 1.2593936055335249, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.7947661548413665,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.747139587392371, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-CC_r1i1p1': {'value': 0.765124633737896,\n", + " 'value_error': 0.06332216523962127},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 14.890119471330937,\n", + " 'value_error': 20.500797523147547},\n", + " 'HadISST': {'value': 0.18969393556645933,\n", + " 'value_error': 16.38280721837829},\n", + " 'Tropflux': {'value': 15.360348911521232,\n", + " 'value_error': 20.387531255084617}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-CC_r1i1p1': {'value': 1.3873504339063352,\n", + " 'value_error': 0.23003112137463932},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 32.21474499980235,\n", + " 'value_error': 32.811324265543014},\n", + " 'HadISST': {'value': 16.624903211655084,\n", + " 'value_error': 27.416614324871315},\n", + " 'Tropflux': {'value': 32.4302806693019,\n", + " 'value_error': 32.706994633048154}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': 8.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.18796992481202,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.16326530612244, 'value_error': None},\n", + " 'Tropflux': {'value': 74.21875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16720343902743445,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1678368624285184, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16585470522085982, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-CC_r1i1p1': {'value': -0.39970109167462275,\n", + " 'value_error': -0.03307949771507127},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 202.2302818114925,\n", + " 'value_error': -24.624665140327103},\n", + " 'HadISST': {'value': 202.51398625132998,\n", + " 'value_error': -16.82658775596605},\n", + " 'Tropflux': {'value': 200.56703881278816,\n", + " 'value_error': -24.224032361424957}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14324754238932466,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10276356122976375, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1394750267260021, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4703142639775728,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7756268946001004, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7411600269762827,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7342504907550685, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17336087833662858,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18907667031372588, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17512592701296692, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.514597944480628,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.199702700718536, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r2i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9825395326990355,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6639435148507957, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0635414733912527,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6237295948826471, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1591442591915853,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9510693142338065, 'value_error': None},\n", + " 'Tropflux': {'value': 1.2086335498343426, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.560496208088347,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.487875073153855, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-CC_r2i1p1': {'value': 0.7141643172614786,\n", + " 'value_error': 0.10529778395305897},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.558772989676324,\n", + " 'value_error': 24.273730783302945},\n", + " 'HadISST': {'value': 6.837453738832735,\n", + " 'value_error': 21.31752855482482},\n", + " 'Tropflux': {'value': 20.997683295660266,\n", + " 'value_error': 24.13961917644059}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': 14.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-CC_r2i1p1': {'value': 1.3495325944415204,\n", + " 'value_error': 0.4001541668874441},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.06250590362896,\n", + " 'value_error': 40.53541680816036},\n", + " 'HadISST': {'value': 18.897628219443487,\n", + " 'value_error': 37.26991844643981},\n", + " 'Tropflux': {'value': 34.272166277926324,\n", + " 'value_error': 40.406527004615846}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': 5.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 84.9624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 89.79591836734694, 'value_error': None},\n", + " 'Tropflux': {'value': 84.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17524006923652607,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.169805249678168, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17358431565520835, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-CC_r2i1p1': {'value': -0.1229219899696319,\n", + " 'value_error': -0.01812385865557517},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 131.439367910595,\n", + " 'value_error': -9.606482444686085},\n", + " 'HadISST': {'value': 131.52661689498464,\n", + " 'value_error': -7.213945763266397},\n", + " 'Tropflux': {'value': 130.9278628297677,\n", + " 'value_error': -9.4501890804776}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21878832571917434,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18795011196913913, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21449277341014863, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6037084345750379,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9064003107549148, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8138456260523285,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8271855341468995, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1752123575958811,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1947664602922102, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17387397604237687, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.02760980092572,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.706068482737693, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-CC_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-CC_r3i1p1',\n", + " 'nyears': 46,\n", + " 'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-CC_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9243504388282304,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6748629498634313, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9240009694553899,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7343631286039018, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0795057188833477,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8787402118216597, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1288303720647945, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.465844079404597,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.384905282179934, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-CC_r3i1p1': {'value': 0.6336474155455001,\n", + " 'value_error': 0.0934262144605235},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 29.51520124125061,\n", + " 'value_error': 21.537041832987132},\n", + " 'HadISST': {'value': 17.340862267677345,\n", + " 'value_error': 18.914130191184704},\n", + " 'Tropflux': {'value': 29.904627559985574,\n", + " 'value_error': 21.418050347373782}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': 13.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-CC_r3i1p1': {'value': 1.5087977296319226,\n", + " 'value_error': 0.4473783745492868},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 26.280890287504654,\n", + " 'value_error': 45.319205406184274},\n", + " 'HadISST': {'value': 9.326329045867048,\n", + " 'value_error': 41.66832914385905},\n", + " 'Tropflux': {'value': 26.5152937380301,\n", + " 'value_error': 45.175104668075655}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': 11.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 66.9172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 77.55102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13177514043249314,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12985089560108032, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12995295597489104, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-CC_r3i1p1': {'value': -0.0004900185991242842,\n", + " 'value_error': -7.224930080716682e-05},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 100.12533050453145,\n", + " 'value_error': -0.03829546748486633},\n", + " 'HadISST': {'value': 100.12567831557091,\n", + " 'value_error': -0.028757812966969727},\n", + " 'Tropflux': {'value': 100.12329143078058,\n", + " 'value_error': -0.03767241659380376}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2151805366796901,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1879065547346746, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20990467107320224, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6733928386459116,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9892346253134696, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9810621338531327,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9963737346216046, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1919090760804908,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.211870906349441, 'value_error': None},\n", + " 'Tropflux': {'value': 0.19024104134899256, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.372698970262609,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.074385658879744, 'value_error': None}}}}}},\n", + " 'HadGEM2-ES': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r1i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r1i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9634434994167944,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6770142140828663, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3469546789938933,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7769195651214647, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2023811704180314,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.997656012772172, 'value_error': None},\n", + " 'Tropflux': {'value': 1.2515654530882019, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.615914924555113,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.684892801059148, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-ES_r1i1p1': {'value': 0.7918552344330884,\n", + " 'value_error': 0.06553440549374324},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 11.91669771582812,\n", + " 'value_error': 21.21701891814706},\n", + " 'HadISST': {'value': 3.2973058537871665,\n", + " 'value_error': 16.95516139273206},\n", + " 'Tropflux': {'value': 12.403355218645867,\n", + " 'value_error': 21.099795549174914}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': 25.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 92.3076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 92.3076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-ES_r1i1p1': {'value': 1.33733786833274,\n", + " 'value_error': 0.2217387344905815},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.658334180853586,\n", + " 'value_error': 31.62850955176771},\n", + " 'HadISST': {'value': 19.63049025976024,\n", + " 'value_error': 26.428273392243497},\n", + " 'Tropflux': {'value': 34.866100009697995,\n", + " 'value_error': 31.527940895922224}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': 7.25, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 78.19548872180451,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 85.20408163265306, 'value_error': None},\n", + " 'Tropflux': {'value': 77.34375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1754839820250079,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1856297229566208, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1749622192591237, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-ES_r1i1p1': {'value': -0.49747367727258285,\n", + " 'value_error': -0.04117121447354635},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 227.2372662488891,\n", + " 'value_error': -30.64820930971271},\n", + " 'HadISST': {'value': 227.5903688395105,\n", + " 'value_error': -20.942611019247597},\n", + " 'Tropflux': {'value': 225.16717029969695,\n", + " 'value_error': -30.1495760412336}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20477362377989475,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16936619412087395, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20032153712428094, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.557965008762284,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.853529237158138, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7245534993887159,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6979116523011123, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17691290090686804,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19461899691135215, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17787641631500975, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.301395510315637,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.002593800641284, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r2i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r2i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9421082584867426,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6746645224053416, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2947815665179798,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7710450408116564, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1556961998095967,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9524412637962045, 'value_error': None},\n", + " 'Tropflux': {'value': 1.2048921181885248, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.579124755155107,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.638419941383335, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-ES_r2i1p1': {'value': 0.7912106610838043,\n", + " 'value_error': 0.06548106022379288},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 11.988397878560459,\n", + " 'value_error': 21.199748179316014},\n", + " 'HadISST': {'value': 3.213221430889008,\n", + " 'value_error': 16.941359823087343},\n", + " 'Tropflux': {'value': 12.474659240211356,\n", + " 'value_error': 21.082620230638216}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': 21.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 61.53846153846154,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.53846153846154, 'value_error': None},\n", + " 'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-ES_r2i1p1': {'value': 1.4743019070915377,\n", + " 'value_error': 0.2444482033123665},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 27.96633908990826,\n", + " 'value_error': 34.86775709773901},\n", + " 'HadISST': {'value': 11.39941200515946,\n", + " 'value_error': 29.134936492822593},\n", + " 'Tropflux': {'value': 28.195383346372516,\n", + " 'value_error': 34.756888659315536}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': 8.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 75.93984962406014,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 83.6734693877551, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17740105641278578,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18771257955851345, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1769090123198803, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-ES_r2i1p1': {'value': -0.6479673951684467,\n", + " 'value_error': -0.0536261631863744},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 265.7285676533837,\n", + " 'value_error': -39.91978120705758},\n", + " 'HadISST': {'value': 266.18848940668437,\n", + " 'value_error': -27.278084711067713},\n", + " 'Tropflux': {'value': 263.032234678942,\n", + " 'value_error': -39.270303425856916}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18439670318111262,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14803073523125862, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1805809787406033, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5281894656752955,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.812918793167366, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.757482518465412,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7124412204139632, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1841452975733409,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20201536797585434, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1857741402102519, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.265345103334888,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.946219860267455, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r3i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r3i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9492639999072388,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.669736126631814, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3544574794791981,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8158866838498879, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.117437729444759,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.915394900973624, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1665926614864532, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.402034097729748,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.491114018907394, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-ES_r3i1p1': {'value': 0.7116259748758741,\n", + " 'value_error': 0.058894584728967414},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.84112962499237,\n", + " 'value_error': 19.067351095299344},\n", + " 'HadISST': {'value': 7.168579831539739,\n", + " 'value_error': 15.237296832316755},\n", + " 'Tropflux': {'value': 21.278479919356812,\n", + " 'value_error': 18.962004574122705}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': 26.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 100.0, 'value_error': None},\n", + " 'HadISST': {'value': 100.0, 'value_error': None},\n", + " 'Tropflux': {'value': 100.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-ES_r3i1p1': {'value': 1.3486847032097546,\n", + " 'value_error': 0.22362010857388595},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 34.10393344922451,\n", + " 'value_error': 31.896866175615806},\n", + " 'HadISST': {'value': 18.948583631851736,\n", + " 'value_error': 26.652507866842086},\n", + " 'Tropflux': {'value': 34.31346209703099,\n", + " 'value_error': 31.79544423059134}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': 8.5, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 74.43609022556392,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 82.6530612244898, 'value_error': None},\n", + " 'Tropflux': {'value': 73.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18274208272524706,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1901227012276355, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18208304989140556, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-ES_r3i1p1': {'value': -0.28632832475902814,\n", + " 'value_error': -0.023696700764422287},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 173.23328842583308,\n", + " 'value_error': -17.64002967277748},\n", + " 'HadISST': {'value': 173.4365218386942,\n", + " 'value_error': -12.053829183680527},\n", + " 'Tropflux': {'value': 172.04181411814247,\n", + " 'value_error': -17.35303392816744}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21513856837812584,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17841988456886324, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21075960438777894, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5717606166132982,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.861730921453577, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7016671207774291,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6521837705252663, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17768803933513067,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19590869923547444, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17851946416700024, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.448520243546338,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.151935197777128, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r4i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r4i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9842540399949022,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.709371640231596, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2438232491932895,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.757156703960924, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1152473977733062,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9101899344253925, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1645126261747079, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.64490116879139,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.683625770722712, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-ES_r4i1p1': {'value': 0.7212699500213061,\n", + " 'value_error': 0.05969272579095686},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 19.768366396288968,\n", + " 'value_error': 19.32575237145339},\n", + " 'HadISST': {'value': 5.910525825042933,\n", + " 'value_error': 15.443793105812203},\n", + " 'Tropflux': {'value': 20.2116436740936,\n", + " 'value_error': 19.21897819127082}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': 21.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 61.53846153846154,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.53846153846154, 'value_error': None},\n", + " 'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-ES_r4i1p1': {'value': 1.3154697229670833,\n", + " 'value_error': 0.21811286327743618},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 35.726800931405315,\n", + " 'value_error': 31.111320245344043},\n", + " 'HadISST': {'value': 20.94469227525931,\n", + " 'value_error': 25.99611833405658},\n", + " 'Tropflux': {'value': 35.93116937395357,\n", + " 'value_error': 31.012396087898946}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': 6.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 81.95488721804512,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 87.75510204081633, 'value_error': None},\n", + " 'Tropflux': {'value': 81.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17674111357902667,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18850753322327982, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17642039000360663, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-ES_r4i1p1': {'value': -0.2964612837771815,\n", + " 'value_error': -0.024535310419661307},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 175.8249632488115,\n", + " 'value_error': -18.26429797701771},\n", + " 'HadISST': {'value': 176.0353889499151,\n", + " 'value_error': -12.480405762273824},\n", + " 'Tropflux': {'value': 174.59132349925915,\n", + " 'value_error': -17.967145653868098}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16460880961538119,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1305241822748552, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16010070447944627, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5587324272470273,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8549659563699084, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8200826429470754,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7936345244421517, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18448297672026123,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20199211660022542, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18596305261195267, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.4747690355237575,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.15568176274605, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadGEM2-ES_r5i1p1': {'keyerror': None,\n", + " 'name': 'HadGEM2-ES_r5i1p1',\n", + " 'nyears': 146,\n", + " 'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"HadGEM2-ES_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9454748985612494,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.673575374166771, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2830975113821441,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7793421679606303, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.154816487422248,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9501364650548555, 'value_error': None},\n", + " 'Tropflux': {'value': 1.204027641824973, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.186243888349914,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.259544444889508, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadGEM2-ES_r5i1p1': {'value': 0.7097890054961646,\n", + " 'value_error': 0.058742556061384434},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 21.04546789557987,\n", + " 'value_error': 19.018131222289025},\n", + " 'HadISST': {'value': 7.408212001169506,\n", + " 'value_error': 15.197963743448836},\n", + " 'Tropflux': {'value': 21.48168922736099,\n", + " 'value_error': 18.9130566393784}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': 22.0, 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.23076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.23076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadGEM2-ES_r5i1p1': {'value': 1.2792117856585878,\n", + " 'value_error': 0.21210107723263677},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 37.49834578854519,\n", + " 'value_error': 30.25380731338853},\n", + " 'HadISST': {'value': 23.123672407863392,\n", + " 'value_error': 25.279594333265088},\n", + " 'Tropflux': {'value': 37.69708127881156,\n", + " 'value_error': 30.157609775824007}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': 11.0, 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 66.9172932330827,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 77.55102040816327, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17541800151046483,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1826850099661982, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17469497295845793, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadGEM2-ES_r5i1p1': {'value': -0.34417961105425476,\n", + " 'value_error': -0.02848450728453739},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 188.0297286265338,\n", + " 'value_error': -21.20411439165742},\n", + " 'HadISST': {'value': 188.2740243910225,\n", + " 'value_error': -14.489248465533722},\n", + " 'Tropflux': {'value': 186.59752255978438,\n", + " 'value_error': -20.85913251172206}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1970630917551254,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16121154883633557, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1934148462000116, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5625213045664244,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8584126027436902, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.747818583724793,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7193694543167486, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1819315994668804,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19892240113903606, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18361837036195414, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.304037891054174,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.002065264943239, 'value_error': None}}}}}},\n", + " 'INMCM4': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'INMCM4_r1i1p1': {'keyerror': None,\n", + " 'name': 'INMCM4_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"INMCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1250380284950356,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.5625005961198295, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6911811722539967,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8358278478670285, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.410591128124567,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2527015571058957, 'value_error': None},\n", + " 'Tropflux': {'value': 1.457480167163873, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.2651244636026115,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.749885733253258, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'INMCM4_r1i1p1': {'value': 0.6277034697811947,\n", + " 'value_error': 0.050256498876555006},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 30.176385696124946,\n", + " 'value_error': 16.630446008506865},\n", + " 'HadISST': {'value': 18.116248420214873,\n", + " 'value_error': 13.219552183204076},\n", + " 'Tropflux': {'value': 30.562158991331422,\n", + " 'value_error': 16.53856331206645}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'INMCM4_r1i1p1': {'value': 1.1189436797528791,\n", + " 'value_error': 0.17946291197842962},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 45.32896605701487,\n", + " 'value_error': 26.167090182077686},\n", + " 'HadISST': {'value': 32.75524674942972,\n", + " 'value_error': 21.747929175624712},\n", + " 'Tropflux': {'value': 45.50280265175954,\n", + " 'value_error': 26.083887112306336}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': 27.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 17.293233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 43.87755102040816, 'value_error': None},\n", + " 'Tropflux': {'value': 14.0625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2842806817590433,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31168072304532934, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2874499936571269, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'INMCM4_r1i1p1': {'value': 0.03126856836623799,\n", + " 'value_error': 0.0025034890623069194},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 92.00253733976633,\n", + " 'value_error': 1.9048193408785528},\n", + " 'HadISST': {'value': 91.98034317553302,\n", + " 'value_error': 1.2947168374317939},\n", + " 'Tropflux': {'value': 92.13265263966896,\n", + " 'value_error': 1.8738287441945427}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12369925557666306,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10049974803851001, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12284172461257503, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1139415287062409,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.276589260142767, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5610026692395598,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7029514492121425, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.46435702008200286,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4854333329922383, 'value_error': None},\n", + " 'Tropflux': {'value': 0.45398682099513527, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'INMCM4_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.256882696859938,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.176237088454591, 'value_error': None}}}}}},\n", + " 'IPSL-CM5A-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5405333407940345,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6079194227983131, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.262621047532487,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3838891897750933, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.432009165611351,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.240336391957861, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4805414422158794, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.807242502147613,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.986002727249759, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': 0.7009576314046778,\n", + " 'value_error': 0.05612152570620891},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.027840127092556,\n", + " 'value_error': 18.57124996838051},\n", + " 'HadISST': {'value': 8.560262415145283,\n", + " 'value_error': 14.762298493904028},\n", + " 'Tropflux': {'value': 22.45863388287882,\n", + " 'value_error': 18.468644390484954}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': 16.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': 0.9977073100386973,\n", + " 'value_error': 0.1600182943982081},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 51.2525150288739,\n", + " 'value_error': 23.331913508700037},\n", + " 'HadISST': {'value': 40.04114497106816,\n", + " 'value_error': 19.39156394494913},\n", + " 'Tropflux': {'value': 51.407516611587425,\n", + " 'value_error': 23.25772540394497}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': 40.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 21.804511278195488,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 17.346938775510203, 'value_error': None},\n", + " 'Tropflux': {'value': 26.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2246873337251727,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23263428719625098, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22433019859349587, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': 0.06384016244398671,\n", + " 'value_error': 0.0051113036753862254},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 83.67180392178402,\n", + " 'value_error': 3.8890164309356616},\n", + " 'HadISST': {'value': 83.62649071673516,\n", + " 'value_error': 2.6433871948501637},\n", + " 'Tropflux': {'value': 83.93745669440027,\n", + " 'value_error': 3.825743795509233}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14710685888720343,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10552100120860594, 'value_error': None},\n", + " 'Tropflux': {'value': 0.144490876572676, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1882511039383263,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2642161664577538, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8837845969566617,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.618408014446847, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07880087835065193,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07771444847039925, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07706466602078249, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3231675163017207,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.579137291889918, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5459385384307247,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6134930058619228, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2057789031629254,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3358858042708253, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3603437337025752,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.169999617619099, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4088691463678489, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.939078211220068,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.09657710099045, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': 0.6854590707801911,\n", + " 'value_error': 0.05488064775649127},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 23.751847674297306,\n", + " 'value_error': 18.16062936791618},\n", + " 'HadISST': {'value': 10.582045548608109,\n", + " 'value_error': 14.435895915611171},\n", + " 'Tropflux': {'value': 24.173116342058886,\n", + " 'value_error': 18.060292456054317}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': 17.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.76923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 30.76923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': 0.9844189661281013,\n", + " 'value_error': 0.15788702994163328},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 51.90177693019024,\n", + " 'value_error': 23.02115855313711},\n", + " 'HadISST': {'value': 40.839729764517365,\n", + " 'value_error': 19.133290032277486},\n", + " 'Tropflux': {'value': 52.05471406542797,\n", + " 'value_error': 22.9479585508447}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': 50.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 51.8796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 3.061224489795918, 'value_error': None},\n", + " 'Tropflux': {'value': 57.8125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23958545044849852,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24973565482342075, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23952115170890348, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': -0.17639485664063426,\n", + " 'value_error': -0.014122891367288889},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 145.11595359651798,\n", + " 'value_error': -10.745625787056307},\n", + " 'HadISST': {'value': 145.2411571674759,\n", + " 'value_error': -7.303864643051251},\n", + " 'Tropflux': {'value': 144.3819363110365,\n", + " 'value_error': -10.570799047460946}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.15061106866190854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11260296787986455, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1470362179305823, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1773318154726762,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.253455711707639, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8829345848940331,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6147107144738587, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0768670196074561,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07674136934297017, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07562653521241937, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.354048930519101,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.616519879786517, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.524282264058674,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.601775151454028, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.214535331798137,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.336013204670561, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3938382937458718,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2024117289344545, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4423920903121874, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.004869562367099,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.163849330316752, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': 0.6966079779035138,\n", + " 'value_error': 0.0557732747137923},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.511680894336752,\n", + " 'value_error': 18.45600976151653},\n", + " 'HadISST': {'value': 9.127673563711355,\n", + " 'value_error': 14.670693979661758},\n", + " 'Tropflux': {'value': 22.939801444947435,\n", + " 'value_error': 18.354040882176122}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': 0.9004049680840102,\n", + " 'value_error': 0.14441236002860194},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.00665925971802,\n", + " 'value_error': 21.0564467422065},\n", + " 'HadISST': {'value': 45.88868859086017,\n", + " 'value_error': 17.500383468448025},\n", + " 'Tropflux': {'value': 56.14654416757815,\n", + " 'value_error': 20.9894939020077}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': 44.75, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 34.58646616541353,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 8.673469387755102, 'value_error': None},\n", + " 'Tropflux': {'value': 39.84375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.223893111570631,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23799927869565493, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22408827571193046, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': 0.13756202479905275,\n", + " 'value_error': 0.011013776532380963},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 64.81619676632083,\n", + " 'value_error': 8.380006476106432},\n", + " 'HadISST': {'value': 64.7185564095607,\n", + " 'value_error': 5.695939373126202},\n", + " 'Tropflux': {'value': 65.38862220973411,\n", + " 'value_error': 8.243667351793125}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1587388019457323,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1254196640305073, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1563799288434741, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1754211711908722,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2464745700360909, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8829169529449794,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6146946292129577, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07302912762248787,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07460956729930965, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07217945180314574, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3165891822228932,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5910494534940414, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r4i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5315496640607027,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.598979538813418, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2358744675728093,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.355688614083777, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4480232142743983,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2558600918996237, 'value_error': None},\n", + " 'Tropflux': {'value': 1.49656661205667, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.823287612833675,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.998326435548866, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': 0.7127286890468555,\n", + " 'value_error': 0.0570639645704965},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.718467424337558,\n", + " 'value_error': 18.8831136874858},\n", + " 'HadISST': {'value': 7.024731059648093,\n", + " 'value_error': 15.01019916395531},\n", + " 'Tropflux': {'value': 21.15649542354177,\n", + " 'value_error': 18.778785072251456}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': 16.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': 0.8907543502583328,\n", + " 'value_error': 0.14286453594350634},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 56.47818366641104,\n", + " 'value_error': 20.830761936503826},\n", + " 'HadISST': {'value': 46.46865827668614,\n", + " 'value_error': 17.312812854509527},\n", + " 'Tropflux': {'value': 56.61656927580751,\n", + " 'value_error': 20.764526702599976}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': 42.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 26.31578947368421,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 14.285714285714285, 'value_error': None},\n", + " 'Tropflux': {'value': 31.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23275070487445781,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24395013722628903, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23269982299962175, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': 0.174073125802805,\n", + " 'value_error': 0.013937004130941922},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 55.47786814374119,\n", + " 'value_error': 10.6041905364106},\n", + " 'HadISST': {'value': 55.35431251761135,\n", + " 'value_error': 7.207730276664082},\n", + " 'Tropflux': {'value': 56.202224203268244,\n", + " 'value_error': 10.431664887903299}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18002216841204574,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1348962813948056, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17515355466944435, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1669523716170698,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2407925255588916, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8816955419436124,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6214761044357078, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07471980305891313,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07588183821503658, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07315518635613329, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3040655035185496,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.560363221261442, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r5i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5149322841496928,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5867409528830747, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2450537351238915,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.363022586886896, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4249949175808119,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2343491138735574, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4734562195213436, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.063448177674829,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 7.214849836173432, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': 0.7266440166121637,\n", + " 'value_error': 0.05817808242680942},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 19.17057337625965,\n", + " 'value_error': 19.251787934015315},\n", + " 'HadISST': {'value': 5.209480259925381,\n", + " 'value_error': 15.303258558640657},\n", + " 'Tropflux': {'value': 19.617153441888824,\n", + " 'value_error': 19.145422405046855}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': 1.0410279000102491,\n", + " 'value_error': 0.166966310965615},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.135892460982085,\n", + " 'value_error': 24.3449884337729},\n", + " 'HadISST': {'value': 37.43772315814054,\n", + " 'value_error': 20.233548344696274},\n", + " 'Tropflux': {'value': 49.29762423394491,\n", + " 'value_error': 24.267579071205525}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': 46.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 39.849624060150376,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 5.1020408163265305, 'value_error': None},\n", + " 'Tropflux': {'value': 45.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23613562915367128,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24686933065923164, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23606512229426385, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': 0.2701653475398139,\n", + " 'value_error': 0.02163053916183007},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 30.90066504702384,\n", + " 'value_error': 16.45793863030413},\n", + " 'HadISST': {'value': 30.70890397810569,\n", + " 'value_error': 11.186557064380656},\n", + " 'Tropflux': {'value': 32.024881698285604,\n", + " 'value_error': 16.19017500180867}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12664356691803136,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09460891725690207, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12333807396762361, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1428765506063348,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2208637704475904, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8836440914191706,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6272147425763119, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0781655890874871,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07726322611973192, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0761812105581458, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.297032865680567,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5580612954798565, 'value_error': None}}}}},\n", + " 'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-LR_r6i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-LR_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5666243971856213,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6146261647419118, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.238622108571064,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.35321645995551, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4135838425814964,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.2223249455448528, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4621148270412156, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 6.665876962270249,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 6.8534824748569845, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': 0.7330471926294403,\n", + " 'value_error': 0.058690746803877175},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.458305698806807,\n", + " 'value_error': 19.421434396341557},\n", + " 'HadISST': {'value': 4.374187642372893,\n", + " 'value_error': 15.438110640194601},\n", + " 'Tropflux': {'value': 18.908821021178838,\n", + " 'value_error': 19.314131575950352}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': 16.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 23.076923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 23.076923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': 0.910991725444446,\n", + " 'value_error': 0.14611032779828673},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 55.48939553905752,\n", + " 'value_error': 21.30402366640655},\n", + " 'HadISST': {'value': 45.25246006630799,\n", + " 'value_error': 17.706149007358015},\n", + " 'Tropflux': {'value': 55.63092518193178,\n", + " 'value_error': 21.236283609900745}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': 43.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 30.82706766917293,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 11.224489795918368, 'value_error': None},\n", + " 'Tropflux': {'value': 35.9375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24434891402523806,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25592668409945746, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24441120657229126, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': 0.1631644411487714,\n", + " 'value_error': 0.013063610363895778},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 58.26794785481285,\n", + " 'value_error': 9.939655042838584},\n", + " 'HadISST': {'value': 58.152135120392835,\n", + " 'value_error': 6.756041618252044},\n", + " 'Tropflux': {'value': 58.94691050973378,\n", + " 'value_error': 9.777941102833783}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1491602840123698,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11346511911159744, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14676064657383936, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.188658304355536,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2657378651795574, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8855449980363123,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6151895512516913, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07764375900865156,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07526057241830968, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0772351562946695, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4301368736168385,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.6764493085672707, 'value_error': None}}}}}},\n", + " 'IPSL-CM5A-MR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4500233304779542,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5132452149567213, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.4689250717449127,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4810174514164895, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0220596544561047,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8787155276940066, 'value_error': None},\n", + " 'Tropflux': {'value': 1.067561126307594, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.775698994561712,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.402183965512219, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': 0.7766568642105679,\n", + " 'value_error': 0.06218231490304325},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 13.607313096422851,\n", + " 'value_error': 20.57683391792054},\n", + " 'HadISST': {'value': 1.3146824788482356,\n", + " 'value_error': 16.35653845987842},\n", + " 'Tropflux': {'value': 14.084630001896837,\n", + " 'value_error': 20.463147551143724}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': 1.0301502463602321,\n", + " 'value_error': 0.16522168750078076},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.667369230260995,\n", + " 'value_error': 24.090608745876313},\n", + " 'HadISST': {'value': 38.09143357169962,\n", + " 'value_error': 20.022128909153466},\n", + " 'Tropflux': {'value': 49.82741107521087,\n", + " 'value_error': 24.014008230252678}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': 67.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 101.50375939849626,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 36.734693877551024, 'value_error': None},\n", + " 'Tropflux': {'value': 109.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29181809811636483,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3082825176342241, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2937434337995267, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': 0.19550644733988756,\n", + " 'value_error': 0.015653043234763842},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.995935403172204,\n", + " 'value_error': 11.909866092928434},\n", + " 'HadISST': {'value': 49.85716658746848,\n", + " 'value_error': 8.095205582572659},\n", + " 'Tropflux': {'value': 50.809480165777785,\n", + " 'value_error': 11.716097661074784}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12596578510409084,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10420636599681496, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12481098916148746, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.069650328972458,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.171784193202519, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9676421588096707,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6822386174696072, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10127623495983053,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.0888707543705843, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10670568114744111, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8875141118174554,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.131172866768149, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4289660494379433,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.499959574378846, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.4632879714706704,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.475142522220454, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.999866764247787,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8583613989935591, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0452672599661623, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.853090429710968,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.477986061921993, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': 0.7762294976070714,\n", + " 'value_error': 0.06214809819043532},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 13.654851914233273,\n", + " 'value_error': 20.56551121927787},\n", + " 'HadISST': {'value': 1.2589326184780143,\n", + " 'value_error': 16.347538039475694},\n", + " 'Tropflux': {'value': 14.131906169218883,\n", + " 'value_error': 20.45188741005855}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': 0.9735935720773923,\n", + " 'value_error': 0.1561507881854054},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 52.43070032133286,\n", + " 'value_error': 22.76800098363013},\n", + " 'HadISST': {'value': 41.49029955185544,\n", + " 'value_error': 18.922886320836852},\n", + " 'Tropflux': {'value': 52.58195564749245,\n", + " 'value_error': 22.69560594233156}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': 54.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 62.40601503759399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28503889841840285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.30157076859229587, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28695775718834854, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': 0.14214799554682028,\n", + " 'value_error': 0.011380948047003039},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 63.64325755827588,\n", + " 'value_error': 8.659374743778155},\n", + " 'HadISST': {'value': 63.5423621184319,\n", + " 'value_error': 5.885827617242786},\n", + " 'Tropflux': {'value': 64.23476622136846,\n", + " 'value_error': 8.518490417133105}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13511178045154668,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10162604021389429, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1316753131831724, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0554838573400764,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.162009237408064, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9589732857256309,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6820113866552355, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10871412491894751,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09805895327838629, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11382403246859701, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8695718426918753,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.102220359535579, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5A-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5A-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4789126973234958,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.526371132162746, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.475893639596321,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.485969730556649, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0296891546723275,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8832153614828123, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0755382137456773, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.897622450515157,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.528142710170185, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': 0.7713945970560315,\n", + " 'value_error': 0.061760996340900716},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 14.19266992468824,\n", + " 'value_error': 20.437414822744334},\n", + " 'HadISST': {'value': 0.628221120623276,\n", + " 'value_error': 16.245714131831285},\n", + " 'Tropflux': {'value': 14.666752751910003,\n", + " 'value_error': 20.32449874212783}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': 1.0523916982909425,\n", + " 'value_error': 0.16878890522794565},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 48.58066290585181,\n", + " 'value_error': 24.61073687116299},\n", + " 'HadISST': {'value': 36.75479708670213,\n", + " 'value_error': 20.454416548026387},\n", + " 'Tropflux': {'value': 48.74416013317327,\n", + " 'value_error': 24.532482512624572}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': 56.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 68.42105263157895,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 14.285714285714285, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28405754631360797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.300603224152934, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28601643031916224, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': 0.0891441111788733,\n", + " 'value_error': 0.007137240972834206},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 77.19989312646193,\n", + " 'value_error': 5.430482940891214},\n", + " 'HadISST': {'value': 77.13661939353102,\n", + " 'value_error': 3.6911309897321365},\n", + " 'Tropflux': {'value': 77.5708411220573,\n", + " 'value_error': 5.342131304067245}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14164536680330383,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11253065692686405, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14244704003024555, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0697142036725529,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1766250155227598, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9632048322889516,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.686087900342545, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11152900562535066,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10129287082569864, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1164476164932506, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8396249756294059,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.0612364166548023, 'value_error': None}}}}}},\n", + " 'IPSL-CM5B-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'IPSL-CM5B-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"IPSL-CM5B-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4015345957742082,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2108930449104152, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3880401023969844,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8207062635871967, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5648199136533119,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6058998201366794, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5791772539878387, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.196287271389938,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.230451627635246, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': 0.7004436243392919,\n", + " 'value_error': 0.0560803722049974},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 22.08501653730977,\n", + " 'value_error': 18.557631807639943},\n", + " 'HadISST': {'value': 8.627314500849046,\n", + " 'value_error': 14.751473409209542},\n", + " 'Tropflux': {'value': 22.51549439520966,\n", + " 'value_error': 18.4551014696585}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': 1.1621872574202523,\n", + " 'value_error': 0.18639857684966435},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 43.216106272158484,\n", + " 'value_error': 27.178364133656498},\n", + " 'HadISST': {'value': 30.156453117066924,\n", + " 'value_error': 22.588416754604886},\n", + " 'Tropflux': {'value': 43.396661092692625,\n", + " 'value_error': 27.09194553259888}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': 41.25, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 24.06015037593985,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.816326530612246, 'value_error': None},\n", + " 'Tropflux': {'value': 28.90625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10193630577550127,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09136495485002362, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09822826754936735, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': 0.04149465423634253,\n", + " 'value_error': 0.0033222311878229834},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 89.38704375692585,\n", + " 'value_error': 2.5277722665997104},\n", + " 'HadISST': {'value': 89.3575912038016,\n", + " 'value_error': 1.7181415814742265},\n", + " 'Tropflux': {'value': 89.55971201973469,\n", + " 'value_error': 2.4866464920225355}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.12032878304628031,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11385083650455154, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12090566881517924, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9232021105293055,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1685819507322817, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6864071761365607,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6495971574066665, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20926570706661315,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20214590406257713, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21955182716012003, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.8022109358234655,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5809025435492963, 'value_error': None}}}}}},\n", + " 'MIROC-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8989635368239366,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9180047032005041, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9281730572471811,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2341673575684748, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.1645441771001788,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.0031627390941957, 'value_error': None},\n", + " 'Tropflux': {'value': 2.2097171935327538, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 13.827694446409824,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 13.725659777720624, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC-ESM_r1i1p1': {'value': 0.4407361665215621,\n", + " 'value_error': 0.03528713433011459},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 50.97399714598176,\n", + " 'value_error': 11.676913342359093},\n", + " 'HadISST': {'value': 42.506083669955856,\n", + " 'value_error': 9.281985894371521},\n", + " 'Tropflux': {'value': 51.24486428542097,\n", + " 'value_error': 11.61239875968054}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': 21.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 61.53846153846154,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.53846153846154, 'value_error': None},\n", + " 'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC-ESM_r1i1p1': {'value': 1.1141686852800146,\n", + " 'value_error': 0.17869706966814422},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 45.56226992174664,\n", + " 'value_error': 26.05542440903541},\n", + " 'HadISST': {'value': 33.042207863658035,\n", + " 'value_error': 21.65512178639777},\n", + " 'Tropflux': {'value': 45.73536468399867,\n", + " 'value_error': 25.972576401100934}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': 54.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 62.40601503759399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.286505876354357,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3024687484178541, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2875015703266471, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC-ESM_r1i1p1': {'value': 0.1535042893178042,\n", + " 'value_error': 0.012290179224810972},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 60.738694281558935,\n", + " 'value_error': 9.351177699458898},\n", + " 'HadISST': {'value': 60.62973824085589,\n", + " 'value_error': 6.356050128996486},\n", + " 'Tropflux': {'value': 61.37745894794809,\n", + " 'value_error': 9.199038034355125}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2160004875577134,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17611669420792428, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2137007828951638, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.40757907407141675,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7029010300589942, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9671945660739307,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2279618467583233, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5874147642588601,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6129195739863141, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5833471892932828, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.618748571499511,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.241751752424308, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9043388867174413,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9039534920436711, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.9289541995649453,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2240418189561495, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.215145653860411,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.051952837674739, 'value_error': None},\n", + " 'Tropflux': {'value': 2.2605419029042513, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 14.054208071040149,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 13.962113612935116, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC-ESM_r2i1p1': {'value': 0.4808345260554144,\n", + " 'value_error': 0.038497572471498785},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 46.513591038483625,\n", + " 'value_error': 12.739283769417394},\n", + " 'HadISST': {'value': 37.27526327641653,\n", + " 'value_error': 10.126464827239957},\n", + " 'Tropflux': {'value': 46.80910178280069,\n", + " 'value_error': 12.668899623204185}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': 34.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 161.53846153846155,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 161.53846153846155, 'value_error': None},\n", + " 'Tropflux': {'value': 161.53846153846155, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC-ESM_r2i1p1': {'value': 1.104766810972681,\n", + " 'value_error': 0.17718914056341756},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.02164084334427,\n", + " 'value_error': 25.8355566021634},\n", + " 'HadISST': {'value': 33.607228900310346,\n", + " 'value_error': 21.472385782563173},\n", + " 'Tropflux': {'value': 46.1932749513709,\n", + " 'value_error': 25.753407704307584}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': 54.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 62.40601503759399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31857122742060123,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.33668213179553275, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32003801403620213, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC-ESM_r2i1p1': {'value': 0.26451041531381264,\n", + " 'value_error': 0.021177782233208833},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 32.347009145477315,\n", + " 'value_error': 16.113451343605217},\n", + " 'HadISST': {'value': 32.159261899420585,\n", + " 'value_error': 10.952407042486948},\n", + " 'Tropflux': {'value': 33.44769439632774,\n", + " 'value_error': 15.851292376053582}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.31312485683787095,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.275612710897618, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3102245454879532, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4134320860616464,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7183523451023194, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9765282309794348,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2370801074757365, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5895934830741099,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6146882705299371, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5857822902119183, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.594426687891195,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.214304104724028, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8783846800283595,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9123648326984852, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.932880625119194,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2341131781479875, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2059583311677557,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 2.0425493569448205, 'value_error': None},\n", + " 'Tropflux': {'value': 2.251297508454143, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 13.932882073357677,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 13.845336261921586, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC-ESM_r3i1p1': {'value': 0.5178001320436365,\n", + " 'value_error': 0.041457189592088925},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 42.4016618564921,\n", + " 'value_error': 13.71865467328249},\n", + " 'HadISST': {'value': 32.45310975416356,\n", + " 'value_error': 10.904967385964708},\n", + " 'Tropflux': {'value': 42.719890881522744,\n", + " 'value_error': 13.642859533316235}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': 34.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 161.53846153846155,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 161.53846153846155, 'value_error': None},\n", + " 'Tropflux': {'value': 161.53846153846155, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC-ESM_r3i1p1': {'value': 1.2004488547961312,\n", + " 'value_error': 0.19253520178108},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.34666357660522,\n", + " 'value_error': 28.073131839271564},\n", + " 'HadISST': {'value': 27.85705975074319,\n", + " 'value_error': 23.33207394212361},\n", + " 'Tropflux': {'value': 41.533162633580986,\n", + " 'value_error': 27.98386815993733}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': 41.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 24.81203007518797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.306122448979592, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30628889081413285,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3243239685043028, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3077842829891996, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC-ESM_r3i1p1': {'value': 0.1849184101191811,\n", + " 'value_error': 0.014805321808478204},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 52.704004136155646,\n", + " 'value_error': 11.264863806807769},\n", + " 'HadISST': {'value': 52.57275061933713,\n", + " 'value_error': 7.656793759414011},\n", + " 'Tropflux': {'value': 53.47348977772262,\n", + " 'value_error': 11.081589286518573}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3157332785243688,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.27871465856041705, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3117439143714144, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4113123453385065,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6968569543368003, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9482339093116773,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2112719703495523, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5805046941531341,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6057291161201198, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5765030996448324, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.527288099426085,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.14716199173061, 'value_error': None}}}}}},\n", + " 'MIROC-ESM-CHEM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC-ESM-CHEM_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC-ESM-CHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9248537898842214,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9119492370892324, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8739218003398155,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1920780591590965, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.143420419394625,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.982594427770493, 'value_error': None},\n", + " 'Tropflux': {'value': 2.1886836534893757, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 13.837032561722019,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 13.727784022107254, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': 0.49185921059442195,\n", + " 'value_error': 0.03938025366225617},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 45.28724235931846,\n", + " 'value_error': 13.031372995960458},\n", + " 'HadISST': {'value': 35.83709609478175,\n", + " 'value_error': 10.358646740488863},\n", + " 'Tropflux': {'value': 45.58952864190793,\n", + " 'value_error': 12.959375065864226}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': 26.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 100.0, 'value_error': None},\n", + " 'HadISST': {'value': 100.0, 'value_error': None},\n", + " 'Tropflux': {'value': 100.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': 1.207231667786526,\n", + " 'value_error': 0.19362307009178015},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 41.015258693645706,\n", + " 'value_error': 28.231751511038272},\n", + " 'HadISST': {'value': 27.449435493922152,\n", + " 'value_error': 23.463905542941266},\n", + " 'Tropflux': {'value': 41.2028115133214,\n", + " 'value_error': 28.141983471321392}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': 30.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.774436090225564,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 38.775510204081634, 'value_error': None},\n", + " 'Tropflux': {'value': 6.25, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.30856035623585426,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.32718518985871814, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3099367134847116, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': 0.17095557978606568,\n", + " 'value_error': 0.013687400686910486},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 56.275233010862955,\n", + " 'value_error': 10.41427579905486},\n", + " 'HadISST': {'value': 56.15389019241557,\n", + " 'value_error': 7.078644119854324},\n", + " 'Tropflux': {'value': 56.98661628474222,\n", + " 'value_error': 10.244839982167516}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22396154221406514,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18554257834416996, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22000567708824295, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4260509047465993,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7106413524384082, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9775756875652791,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2325704139765468, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5890345332808851,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.614146638730407, 'value_error': None},\n", + " 'Tropflux': {'value': 0.5855032434951192, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 5.461007532460952,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 5.092107827596136, 'value_error': None}}}}}},\n", + " 'MIROC4h': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r1i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4870297556116632,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1263260919985147, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7471149957738579,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.5590858358364366, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7682194519681959,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9734976026186368, 'value_error': None},\n", + " 'Tropflux': {'value': 0.7410101430290212, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.741963510150388,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 12.442749052250731, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC4h_r1i1p1': {'value': 0.7138618252656673,\n", + " 'value_error': 0.09539379898718617},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.592421177813463,\n", + " 'value_error': 23.166724694511164},\n", + " 'HadISST': {'value': 6.876913739664839,\n", + " 'value_error': 20.02234512670869},\n", + " 'Tropflux': {'value': 21.03114557860552,\n", + " 'value_error': 23.038729261824898}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': 14.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC4h_r1i1p1': {'value': 1.8633727162046163,\n", + " 'value_error': 0.5002607789358686},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 8.956532076258092,\n", + " 'value_error': 53.416352533746036},\n", + " 'HadISST': {'value': 11.982435561633928,\n", + " 'value_error': 48.32031987159827},\n", + " 'Tropflux': 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0.3018589696247106, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28849326976442513, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC4h_r1i1p1': {'value': 0.12036944861583379,\n", + " 'value_error': 0.016085044162690746},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 69.21348750403745,\n", + " 'value_error': 8.98179581693552},\n", + " 'HadISST': {'value': 69.12805029181962,\n", + " 'value_error': 6.637761446861187},\n", + " 'Tropflux': {'value': 69.7143709062553,\n", + " 'value_error': 8.83566583721135}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19047703483369127,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16317159952640112, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1883070201991026, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2229171355614603,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6731064497588146, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.502025933471507,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.0020753579162336, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.335733194333027,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3577596859046938, 'value_error': None},\n", + " 'Tropflux': {'value': 0.327769077680785, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.097796616455399,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.016742902524398, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r2i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5769071499145908,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.22054885617193, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8981889906756104,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.7085499193140885, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8499910234059324,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0611496299078669, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8189749207352596, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 13.474932374656737,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 13.17034573290172, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC4h_r2i1p1': {'value': 0.7337495828504664,\n", + " 'value_error': 0.0980514123826695},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.380174182502078,\n", + " 'value_error': 23.812135596805533},\n", + " 'HadISST': {'value': 4.282561023850897,\n", + " 'value_error': 20.580155520913443},\n", + " 'Tropflux': {'value': 18.83112117906922,\n", + " 'value_error': 23.680574288977706}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': 12.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC4h_r2i1p1': {'value': 2.3009268815099877,\n", + " 'value_error': 0.6177312053614226},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 12.422147705540763,\n", + " 'value_error': 65.95949398006212},\n", + " 'HadISST': {'value': 38.27796983393717,\n", + " 'value_error': 59.666819176240914},\n", + " 'Tropflux': {'value': 12.064680836496452,\n", + " 'value_error': 65.74976365309347}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': 17.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 48.87218045112782,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 65.3061224489796, 'value_error': None},\n", + " 'Tropflux': {'value': 46.875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2880847985476854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3052978288171303, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2891856877641837, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC4h_r2i1p1': {'value': 0.28465110832182816,\n", + " 'value_error': 0.038038104360919915},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 27.195687908245596,\n", + " 'value_error': 21.24025791769509},\n", + " 'HadISST': {'value': 26.99364496936542,\n", + " 'value_error': 15.697057470581402},\n", + " 'Tropflux': {'value': 28.38018295429645,\n", + " 'value_error': 20.89468799803642}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10522512910083096,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12209583333633225, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10572548590990648, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2313431940277366,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.680981925104154, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4561291433594967,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9630323584514073, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.32920739057712434,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.35200335657605725, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32119538785111484, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.8525979928637226,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.7753187548275813, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC4h_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC4h_r3i1p1',\n", + " 'nyears': 56,\n", + " 'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC4h_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4893492007990967,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1410587483303827, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7599901121411472,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.561232142205437, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7857169319479281,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9891947860432199, 'value_error': None},\n", + " 'Tropflux': {'value': 0.7586961672713189, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 12.706079001599283,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 12.403816604981143, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC4h_r3i1p1': {'value': 0.603849870079341,\n", + " 'value_error': 0.0806928331030304},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 32.8297795763943,\n", + " 'value_error': 19.596542638679782},\n", + " 'HadISST': {'value': 21.227944191071586,\n", + " 'value_error': 16.936737720842864},\n", + " 'Tropflux': {'value': 33.20089295302055,\n", + " 'value_error': 19.488272350701312}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': 11.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC4h_r3i1p1': {'value': 2.4622341839363053,\n", + " 'value_error': 0.661037472571451},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30354260125905,\n", + " 'value_error': 70.5836079094654},\n", + " 'HadISST': {'value': 47.97199639260047,\n", + " 'value_error': 63.849782886657515},\n", + " 'Tropflux': {'value': 19.921015389439102,\n", + " 'value_error': 70.35917436287158}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': 18.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 44.3609022556391,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 62.244897959183675, 'value_error': None},\n", + " 'Tropflux': {'value': 42.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3292968723864246,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.34633209430736167, 'value_error': None},\n", + " 'Tropflux': {'value': 0.33037290338184305, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC4h_r3i1p1': {'value': 0.6304556642185342,\n", + " 'value_error': 0.08424818189488394},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 61.2496477121944,\n", + " 'value_error': 47.04369848626568},\n", + " 'HadISST': {'value': 61.69713978761029,\n", + " 'value_error': 34.76641581420822},\n", + " 'Tropflux': {'value': 58.626184851205835,\n", + " 'value_error': 46.27831761521694}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1796255081875928,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1916149274126757, 'value_error': None},\n", + " 'Tropflux': {'value': 0.181586044463979, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2278376589067852,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.678365004445241, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.6005327611993192,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.1043814196670008, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3486516794157163,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3714454219056565, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3405643212515411, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC4h_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.311834398026247,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.2156888704853115, 'value_error': None}}}}}},\n", + " 'MIROC5': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r1i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r1i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6809142603705576,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5440271794714036, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.047612699961617,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2705284824420011, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.047191659394279,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8909159329938655, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0911821868769613, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.795650977869691,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.1576652750391254, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC5_r1i1p1': {'value': 1.1312816350097514,\n", + " 'value_error': 0.088608816250085},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 25.839948884642112,\n", + " 'value_error': 29.753588454716358},\n", + " 'HadISST': {'value': 47.57539001688474,\n", + " 'value_error': 23.568511558616677},\n", + " 'Tropflux': {'value': 25.144687084836526,\n", + " 'value_error': 29.589200804824156}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': 23.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 76.92307692307693,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 76.92307692307693, 'value_error': None},\n", + " 'Tropflux': {'value': 76.92307692307693, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC5_r1i1p1': {'value': 1.2440253640164545,\n", + " 'value_error': 0.19517945007094034},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.21753692098369,\n", + " 'value_error': 28.879906104449603},\n", + " 'HadISST': {'value': 25.238258051368113,\n", + " 'value_error': 23.917923697591476},\n", + " 'Tropflux': {'value': 39.410805927252525,\n", + " 'value_error': 28.788077138145816}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': 53.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 59.3984962406015,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 8.16326530612245, 'value_error': None},\n", + " 'Tropflux': {'value': 65.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2523972348321122,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2690666919163825, 'value_error': None},\n", + " 'Tropflux': {'value': 0.25288771569919133, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC5_r1i1p1': {'value': 0.6875149139812012,\n", + " 'value_error': 0.05385032408983475},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 75.84351123843742,\n", + " 'value_error': 41.57642713775199},\n", + " 'HadISST': {'value': 76.33150348467755,\n", + " 'value_error': 28.161003522005874},\n", + " 'Tropflux': {'value': 72.98261245431573,\n", + " 'value_error': 40.89999643519835}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26800494175538864,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24719705542111253, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2629154152387304, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5879945664262926,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3499852143388839, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.680542239478116,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3121652961180728, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2141003891725785,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2251243030340837, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2183658139696359, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2912347232535222,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.342725873114606, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r2i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r2i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7108994796294356,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5179001892570765, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.207329803593066,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3964838901205343, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1347263961213514,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9658472094989241, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1801431716161164, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2347988877030067,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.697535178537527, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC5_r2i1p1': {'value': 1.0874667178822823,\n", + " 'value_error': 0.08517696707954035},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 20.966125460771174,\n", + " 'value_error': 28.601221995256438},\n", + " 'HadISST': {'value': 41.85974566843918,\n", + " 'value_error': 22.655695201662667},\n", + " 'Tropflux': {'value': 20.29779138364546,\n", + " 'value_error': 28.44320113417606}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': 27.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 107.6923076923077,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 107.6923076923077, 'value_error': None},\n", + " 'Tropflux': {'value': 107.6923076923077, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC5_r2i1p1': {'value': 1.2545607085681398,\n", + " 'value_error': 0.1968323767839994},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 38.70278520469227,\n", + " 'value_error': 29.12448291954634},\n", + " 'HadISST': {'value': 24.60511926377133,\n", + " 'value_error': 24.12047870523336},\n", + " 'Tropflux': {'value': 38.897690958595916,\n", + " 'value_error': 29.031876276333517}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': 56.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 68.42105263157895,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 14.285714285714285, 'value_error': None},\n", + " 'Tropflux': {'value': 75.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.26638469484578936,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2836049833641321, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2670500609444636, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC5_r2i1p1': {'value': 0.7818443215061422,\n", + " 'value_error': 0.06123877350834302},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 99.96984492940466,\n", + " 'value_error': 47.28085573872232},\n", + " 'HadISST': {'value': 100.5247913878786,\n", + " 'value_error': 32.024789926516114},\n", + " 'Tropflux': {'value': 96.71642100610846,\n", + " 'value_error': 46.51161642052123}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2778816498377675,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2508669929651064, 'value_error': None},\n", + " 'Tropflux': {'value': 0.27250729342470026, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6264100554214536,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3635050987644913, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7424859933517475,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3860814210949524, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20458441235767325,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21360797542235252, 'value_error': None},\n", + " 'Tropflux': {'value': 0.209421326003357, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2284949890232995,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.3038363156248942, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r3i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r3i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7179890318159114,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5312208529200215, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.229850637417764,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4256912739881427, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1268142897505915,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9598660975463388, 'value_error': None},\n", + " 'Tropflux': {'value': 1.1720456679623712, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.3528629531269494,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.814428422870506, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC5_r3i1p1': {'value': 1.010332610318461,\n", + " 'value_error': 0.07913535749955454},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 12.385987807418317,\n", + " 'value_error': 26.572534866205693},\n", + " 'HadISST': {'value': 31.797621741856446,\n", + " 'value_error': 21.048724797987862},\n", + " 'Tropflux': {'value': 11.765058723701483,\n", + " 'value_error': 26.425722438354104}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': 22.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.23076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.23076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC5_r3i1p1': {'value': 1.2305716938131208,\n", + " 'value_error': 0.1930686571340145},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.874876583071625,\n", + " 'value_error': 28.567580694155666},\n", + " 'HadISST': {'value': 26.046778399181797,\n", + " 'value_error': 23.659260275861083},\n", + " 'Tropflux': {'value': 40.066055457140514,\n", + " 'value_error': 28.476744823863815}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': 44.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2759964406045241,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2958020755524672, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2770189410888194, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC5_r3i1p1': {'value': 1.0281351925225577,\n", + " 'value_error': 0.08052976335436737},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 162.96288066547567,\n", + " 'value_error': 62.174924573114865},\n", + " 'HadISST': {'value': 163.69264229221957,\n", + " 'value_error': 42.113004662059744},\n", + " 'Tropflux': {'value': 158.68458696975384,\n", + " 'value_error': 61.16336511970467}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22722290795831013,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2041422628331093, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22219910949840474, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6004462904385464,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.3553393897350266, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.72058826565502,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.36890215284346634, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.21454414645701925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22453052511965665, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21886709755144143, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3269369766439334,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.306183033544636, 'value_error': None}}}}},\n", + " 'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r4i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r4i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6650620178799438,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5298329396628796, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.112016340638596,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3301315365925093, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1030586496907924,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9429823692047393, 'value_error': None},\n", + " 'Tropflux': {'value': 1.147245752043471, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.1626834638271517,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.556332119554821, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC5_r4i1p1': {'value': 1.1655098018273102,\n", + " 'value_error': 0.09128977318446341},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 29.64737457727211,\n", + " 'value_error': 30.653815911374622},\n", + " 'HadISST': {'value': 52.040445323488996,\n", + " 'value_error': 24.28160272911283},\n", + " 'Tropflux': {'value': 28.93107686905154,\n", + " 'value_error': 30.484454532811206}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': 25.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 92.3076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 92.3076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC5_r4i1p1': {'value': 1.2340515212700078,\n", + " 'value_error': 0.19361461932178226},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.70485393720153,\n", + " 'value_error': 28.64836448934302},\n", + " 'HadISST': {'value': 25.83765246824611,\n", + " 'value_error': 23.72616425547601},\n", + " 'Tropflux': {'value': 39.8965734295038,\n", + " 'value_error': 28.5572717521355}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': 26.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 20.30075187969925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 17.1875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2847581409840898,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3014466536165638, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2854329213004645, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC5_r4i1p1': {'value': 0.7633217578308438,\n", + " 'value_error': 0.05978797435241853},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 95.2323875047486,\n", + " 'value_error': 46.16073164629941},\n", + " 'HadISST': {'value': 95.77418680485518,\n", + " 'value_error': 31.26609513998974},\n", + " 'Tropflux': {'value': 92.05603998927985,\n", + " 'value_error': 45.40971626841609}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.28786358511322435,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26721805145323485, 'value_error': None},\n", + " 'Tropflux': {'value': 0.28239872346342737, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.5827340450845203,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.342994084223525, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6660035770777266,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.30233231674656524, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2109643452309322,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.22103104913880667, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21568221068119214, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r4i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3378452422824008,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.3251422681757423, 'value_error': None}}}}},\n", + " 'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MIROC5_r5i1p1': {'keyerror': None,\n", + " 'name': 'MIROC5_r5i1p1',\n", + " 'nyears': 163,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MIROC5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.767615244603694,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5238724398805301, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.2970384540758384,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4784878397159356, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.157483883937813,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.9840574556580232, 'value_error': None},\n", + " 'Tropflux': {'value': 1.2036664889137823, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.6216979582854,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.167055311809335, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MIROC5_r5i1p1': {'value': 0.8545498028182389,\n", + " 'value_error': 0.0669335063092534},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 4.942765640227834,\n", + " 'value_error': 22.475325648589468},\n", + " 'HadISST': {'value': 11.475795714359409,\n", + " 'value_error': 17.803229789867192},\n", + " 'Tropflux': {'value': 5.467953900643461,\n", + " 'value_error': 22.351150174106557}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': 23.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 76.92307692307693,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 76.92307692307693, 'value_error': None},\n", + " 'Tropflux': {'value': 76.92307692307693, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MIROC5_r5i1p1': {'value': 1.2385553432249015,\n", + " 'value_error': 0.19432123955461694},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 39.48479942737711,\n", + " 'value_error': 28.75292019932352},\n", + " 'HadISST': {'value': 25.56698791066247,\n", + " 'value_error': 23.81275579370389},\n", + " 'Tropflux': {'value': 39.67721862341438,\n", + " 'value_error': 28.661495008030652}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': 57.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 72.93233082706767,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 17.346938775510203, 'value_error': None},\n", + " 'Tropflux': {'value': 79.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3495897677689399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3670091570266416, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3504898844094339, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MIROC5_r5i1p1': {'value': 0.423949710300179,\n", + " 'value_error': 0.033206304086198235},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 8.432274168441092,\n", + " 'value_error': 25.63771909804478},\n", + " 'HadISST': {'value': 8.733190071812897,\n", + " 'value_error': 17.365222255008643},\n", + " 'Tropflux': {'value': 6.66812740440495,\n", + " 'value_error': 25.220604364161918}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2355176932630607,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20896979844346875, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23035731076869959, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6184196139152686,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.39036529802643866, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7417685610918248,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.38455583399971155, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20361720156089277,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.21472023989560818, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20756689298474618, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MIROC5_r5i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4051750779891348,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 1.4121109713334101, 'value_error': None}}}}}},\n", + " 'MPI-ESM-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8527397242476178,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7125523202876818, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9592435509754633,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.823668454454153, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7234000131640341,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.524406051762158, 'value_error': None},\n", + " 'Tropflux': {'value': 1.7715707027596923, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.981132618447539,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.872148982834851, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': 0.7829015584810048,\n", + " 'value_error': 0.06268229058534439},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 12.912674393315799,\n", + " 'value_error': 20.742281547101772},\n", + " 'HadISST': {'value': 2.12930119445038,\n", + " 'value_error': 16.488052886276243},\n", + " 'Tropflux': {'value': 13.39382915599822,\n", + " 'value_error': 20.627681087324625}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': 25.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 92.3076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 92.3076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': 1.056616561779273,\n", + " 'value_error': 0.16946651422475825},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 48.374238168526325,\n", + " 'value_error': 24.70953753996031},\n", + " 'HadISST': {'value': 36.500896995097094,\n", + " 'value_error': 20.53653152269713},\n", + " 'Tropflux': {'value': 48.53839176121751,\n", + " 'value_error': 24.630969026547113}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': 61.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 83.45864661654136,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 24.489795918367346, 'value_error': None},\n", + " 'Tropflux': {'value': 90.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.32086598210740364,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.33142411828344187, 'value_error': None},\n", + " 'Tropflux': {'value': 0.32111377900434374, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': 0.2849078819124486,\n", + " 'value_error': 0.022810886567579083},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 27.13001374052968,\n", + " 'value_error': 17.356024666020513},\n", + " 'HadISST': {'value': 26.9277885459359,\n", + " 'value_error': 11.79699139111742},\n", + " 'Tropflux': {'value': 28.31557727722453,\n", + " 'value_error': 17.07364956150571}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22773398545916596,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1866417035746854, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22347774386549057, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3335184942365097,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1661966761588847, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1523646021069494,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7438777620784196, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23308067258419182,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.24796130785573745, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23443928434972228, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.58979712568747,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8330435615637053, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8088306219379116,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6698961286640313, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.005651471334551,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8568457413153565, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.802527633956328,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.602600694832737, 'value_error': None},\n", + " 'Tropflux': {'value': 1.8507096547046133, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.999671453780639,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.91316379115376, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': 0.8100745315375866,\n", + " 'value_error': 0.06485786958981729},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 9.890044630167095,\n", + " 'value_error': 21.462205337654684},\n", + " 'HadISST': {'value': 5.67400834132171,\n", + " 'value_error': 17.060320768465097},\n", + " 'Tropflux': {'value': 10.387899328187217,\n", + " 'value_error': 21.343627321348162}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': 21.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 61.53846153846154,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.53846153846154, 'value_error': None},\n", + " 'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': 1.0012528386841537,\n", + " 'value_error': 0.16058694759025893},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 51.07928225547427,\n", + " 'value_error': 23.41482757264049},\n", + " 'HadISST': {'value': 39.82807061958766,\n", + " 'value_error': 19.460475282728495},\n", + " 'Tropflux': {'value': 51.234834663611316,\n", + " 'value_error': 23.340375827388947}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': 44.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 32.33082706766917,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 37.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2826534146103482,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2944203260867981, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2828981680948865, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': 0.19902432701637318,\n", + " 'value_error': 0.01593469902371588},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 49.096178464307485,\n", + " 'value_error': 12.124168365042962},\n", + " 'HadISST': {'value': 48.95491268749001,\n", + " 'value_error': 8.240868089274528},\n", + " 'Tropflux': {'value': 49.9243618878126,\n", + " 'value_error': 11.92691332680044}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2119843535586562,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17683764819603867, 'value_error': None},\n", + " 'Tropflux': {'value': 0.20734885117228655, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3168878323576723,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1426285998229986, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1542043261761905,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7438402580795954, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2401095725688853,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2542447743914251, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24224911485526088, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.641633870104341,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8768751407674285, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-LR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-LR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8551803763081658,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.72403818125649, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9221916971334934,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.7858592845191748, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7366210977061691,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.5375017994534301, 'value_error': None},\n", + " 'Tropflux': {'value': 1.7848153027991998, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.209817796431585,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.087608649239206, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': 0.8356383451960697,\n", + " 'value_error': 0.06690461273249237},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 7.046412324536759,\n", + " 'value_error': 22.139495878946626},\n", + " 'HadISST': {'value': 9.008801070399379,\n", + " 'value_error': 17.598699453511752},\n", + " 'Tropflux': {'value': 7.559978002514528,\n", + " 'value_error': 22.017175853485586}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': 22.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.23076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.23076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': 1.0944242769450847,\n", + " 'value_error': 0.17553034279958524},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.52697193293411,\n", + " 'value_error': 25.59369096986358},\n", + " 'HadISST': {'value': 34.22878042364076,\n", + " 'value_error': 21.27136699886681},\n", + " 'Tropflux': {'value': 46.69699924793897,\n", + " 'value_error': 25.51231112821309}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': 71.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 115.0375939849624,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 45.91836734693878, 'value_error': None},\n", + " 'Tropflux': {'value': 123.4375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2978165992412797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3102575136741727, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2982206819840047, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': 0.23152806426488237,\n", + " 'value_error': 0.01853708074240059},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 40.782800572543344,\n", + " 'value_error': 14.104231751272245},\n", + " 'HadISST': {'value': 40.618463919111356,\n", + " 'value_error': 9.586728744044471},\n", + " 'Tropflux': {'value': 41.7462391017671,\n", + " 'value_error': 13.874761927882115}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.19584732087970766,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15577006233267687, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1913077793587819, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.352904416093818,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.190213599995441, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1435919954687672,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7352076660719811, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24513242283052528,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26074681235456953, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24604180488436814, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5673752839177104,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.809841408384925, 'value_error': None}}}}}},\n", + " 'MPI-ESM-MR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8624755655213807,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6486446218551836, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5132536711310993,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3612448661338883, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4436879420239443,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.257438435158783, 'value_error': None},\n", + " 'Tropflux': {'value': 1.4912090976850538, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.838601171606186,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.820787646436989, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': 0.6484460192110592,\n", + " 'value_error': 0.05191723194926248},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 27.86905454249157,\n", + " 'value_error': 17.180001435518268},\n", + " 'HadISST': {'value': 15.41038833433947,\n", + " 'value_error': 13.65639414410062},\n", + " 'Tropflux': {'value': 28.267575770487603,\n", + " 'value_error': 17.085082462452956}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': 24.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 84.61538461538461,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 84.61538461538461, 'value_error': None},\n", + " 'Tropflux': {'value': 84.61538461538461, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': 1.0326732799851528,\n", + " 'value_error': 0.16562634679645488},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 49.54409505708921,\n", + " 'value_error': 24.149611222578535},\n", + " 'HadISST': {'value': 37.93980773331233,\n", + " 'value_error': 20.071166905949354},\n", + " 'Tropflux': {'value': 49.70452887491646,\n", + " 'value_error': 24.072823097742273}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': 52.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 56.390977443609025,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24793498403542114,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26776790315407034, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24903376154059642, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': 0.07154837719460673,\n", + " 'value_error': 0.005728454773961188},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 81.70029825759416,\n", + " 'value_error': 4.358585627985308},\n", + " 'HadISST': {'value': 81.64951382718836,\n", + " 'value_error': 2.9625561221664403},\n", + " 'Tropflux': {'value': 81.99802658487745,\n", + " 'value_error': 4.287673302385276}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2199133029685066,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.17945554814509204, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21565916805221672, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3947921386159559,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2718147444161652, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1675984079665462,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.833093275434345, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22334957394805885,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23874077090886506, 'value_error': None},\n", + " 'Tropflux': {'value': 0.22481906018256342, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0016023386866397,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.1562864066556813, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8634641571855046,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.634989804888192, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.5955702203300186,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4353450373796686, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5129382110568068,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.3252822850482784, 'value_error': None},\n", + " 'Tropflux': {'value': 1.5605235205380554, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.028119291765881,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.022685640226129, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': 0.6620758488594904,\n", + " 'value_error': 0.053008491678403054},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 26.352918318596984,\n", + " 'value_error': 17.541111668272592},\n", + " 'HadISST': {'value': 13.63238066234751,\n", + " 'value_error': 13.943440899391657},\n", + " 'Tropflux': {'value': 26.759816152028087,\n", + " 'value_error': 17.444197572413778}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': 25.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 92.3076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 92.3076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': 1.1270457244709002,\n", + " 'value_error': 0.18076236660191577},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.933104165302545,\n", + " 'value_error': 26.35656078603382},\n", + " 'HadISST': {'value': 32.26834110105183,\n", + " 'value_error': 21.905401529143152},\n", + " 'Tropflux': {'value': 45.10819947564635,\n", + " 'value_error': 26.272755259674035}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': 16.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 51.8796992481203,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 67.3469387755102, 'value_error': None},\n", + " 'Tropflux': {'value': 50.0, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.24054068777659682,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26168392804137935, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24182876421393232, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': 0.5018284031108822,\n", + " 'value_error': 0.0401784278585503},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 28.35106067911183,\n", + " 'value_error': 30.57039378495902},\n", + " 'HadISST': {'value': 28.70725421714449,\n", + " 'value_error': 20.778875303760465},\n", + " 'Tropflux': {'value': 26.26284377050102,\n", + " 'value_error': 30.073026541814517}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23624163613135965,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.19413542052843163, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2326092781576639, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3764640728013484,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2420653822198995, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1828160632665585,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8461289982752908, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22328833140591522,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.23822164506035443, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2247759898840539, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.11685255230353,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.3020232270172514, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-MR_r3i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-MR_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8804029007677021,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6528409226473786, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.6308397635360645,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.466466304635582, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5187818515293403,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.331485525114276, 'value_error': None},\n", + " 'Tropflux': {'value': 1.566329297005618, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.245933959414506,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.25139297579219, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': 0.578247077607553,\n", + " 'value_error': 0.046296818490242204},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 35.67774775359019,\n", + " 'value_error': 15.320142816928218},\n", + " 'HadISST': {'value': 24.567821696033384,\n", + " 'value_error': 12.17799133702879},\n", + " 'Tropflux': {'value': 36.03312619470338,\n", + " 'value_error': 15.235499504827537}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': 21.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 61.53846153846154,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.53846153846154, 'value_error': None},\n", + " 'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': 1.0878608677641721,\n", + " 'value_error': 0.17447766379042132},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.84765685444028,\n", + " 'value_error': 25.440202172306993},\n", + " 'HadISST': {'value': 34.62321924915025,\n", + " 'value_error': 21.14379975790554},\n", + " 'Tropflux': {'value': 47.01666449283176,\n", + " 'value_error': 25.359310376482124}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': 19.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 42.857142857142854,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 61.224489795918366, 'value_error': None},\n", + " 'Tropflux': {'value': 40.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2505180973089456,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2684309505879965, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2514530526458567, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': -0.11550004669267767,\n", + " 'value_error': -0.009247404620649918},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 129.541080994207,\n", + " 'value_error': -7.0360344047647265},\n", + " 'HadISST': {'value': 129.6230619463001,\n", + " 'value_error': -4.7824337022935985},\n", + " 'Tropflux': {'value': 129.06046023030876,\n", + " 'value_error': -6.921561131728601}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22739596221343783,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1833217360383234, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2240984410508071, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4030027550458064,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2752347006886329, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1949998324388011,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8692366750024243, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.23503940015520478,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.25179597181223395, 'value_error': None},\n", + " 'Tropflux': {'value': 0.23494785391143846, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.0580008655105853,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.2049499129403864, 'value_error': None}}}}}},\n", + " 'MPI-ESM-P': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r1i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8014718246755053,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6655517043035408, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.053924224157592,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.9030661780435003, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.819438413116001,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.6206918255122706, 'value_error': None},\n", + " 'Tropflux': {'value': 1.8675558608296168, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.268354577842842,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.17454641015099, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-P_r1i1p1': {'value': 0.7722441471014796,\n", + " 'value_error': 0.06182901478107203},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 14.098168846454781,\n", + " 'value_error': 20.459922896767367},\n", + " 'HadISST': {'value': 0.7390446993113362,\n", + " 'value_error': 16.263605814287587},\n", + " 'Tropflux': {'value': 14.572773789191675,\n", + " 'value_error': 20.34688245974263}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': 19.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 46.15384615384615,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 46.15384615384615, 'value_error': None},\n", + " 'Tropflux': {'value': 46.15384615384615, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-P_r1i1p1': {'value': 0.9831168306329693,\n", + " 'value_error': 0.15767818562535948},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 51.96539862549169,\n", + " 'value_error': 22.99070742539319},\n", + " 'HadISST': {'value': 40.917983729979056,\n", + " 'value_error': 19.10798156408769},\n", + " 'Tropflux': {'value': 52.11813346386278,\n", + " 'value_error': 22.9176042480549}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': 48.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 45.86466165413533,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.0204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 51.5625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.29566027609577245,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.31000073968249187, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2961771525959026, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-P_r1i1p1': {'value': -0.17696261378331826,\n", + " 'value_error': -0.014168348318822709},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 145.2611670420302,\n", + " 'value_error': -10.780212429260049},\n", + " 'HadISST': {'value': 145.3867736021855,\n", + " 'value_error': -7.327373385875526},\n", + " 'Tropflux': {'value': 144.52478719584477,\n", + " 'value_error': -10.604822979776047}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.20064176852141577,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16012238223578149, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1965396989353668, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.3026362985431401,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1284028975341265, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1454552461371574,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7427289534966679, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2402621590276404,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.2549278264008837, 'value_error': None},\n", + " 'Tropflux': {'value': 0.24182152941493462, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.6092831963323477,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.851110722932924, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MPI-ESM-P_r2i1p1': {'keyerror': None,\n", + " 'name': 'MPI-ESM-P_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MPI-ESM-P_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.8167669228713939,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6900532093920426, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.014815822670141,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.8755661589140074, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7103438540249833,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 1.513639654056288, 'value_error': None},\n", + " 'Tropflux': {'value': 1.7583801088803828, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 8.968202768335205,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 8.864259652901818, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MPI-ESM-P_r2i1p1': {'value': 0.7346261231074651,\n", + " 'value_error': 0.05881716241509352},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 18.282670804278236,\n", + " 'value_error': 19.463266757209546},\n", + " 'HadISST': {'value': 4.168216579206071,\n", + " 'value_error': 15.471363210640288},\n", + " 'Tropflux': {'value': 18.734156503974486,\n", + " 'value_error': 19.355732814326917}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': 22.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.23076923076923,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 69.23076923076923, 'value_error': None},\n", + " 'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MPI-ESM-P_r2i1p1': {'value': 0.9561960256500118,\n", + " 'value_error': 0.15336046513373297},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 53.280735822195645,\n", + " 'value_error': 22.361150152306173},\n", + " 'HadISST': {'value': 42.53583360137339,\n", + " 'value_error': 18.58474543458967},\n", + " 'Tropflux': {'value': 53.42928830434032,\n", + " 'value_error': 22.290048767958737}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': 69.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 107.51879699248121,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 40.816326530612244, 'value_error': None},\n", + " 'Tropflux': {'value': 115.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.32440554351732415,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.3356391603028059, 'value_error': None},\n", + " 'Tropflux': {'value': 0.3244937938551036, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MPI-ESM-P_r2i1p1': {'value': -0.43667712410893056,\n", + " 'value_error': -0.03496215084623898},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 211.6875244729845,\n", + " 'value_error': -26.60150672648115},\n", + " 'HadISST': {'value': 211.997474186572,\n", + " 'value_error': -18.081199576619657},\n", + " 'Tropflux': {'value': 209.87041617756915,\n", + " 'value_error': -26.168711579741842}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.22026153294293485,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1802399487459419, 'value_error': None},\n", + " 'Tropflux': {'value': 0.21664277358619957, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.315732598403537,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1399346122192355, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1623188807401688,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.7610085276792554, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.253045057764712,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.26823545904004437, 'value_error': None},\n", + " 'Tropflux': {'value': 0.2545778205358436, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.541317403716177,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.7786733344194685, 'value_error': None}}}}}},\n", + " 'MRI-CGCM3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.391942713056941,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4505443395259134, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.629545543671874,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.6115579981365675, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8904359694820737,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7121475833716178, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9384651338560948, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.255492583826575,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.941059113314827, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MRI-CGCM3_r1i1p1': {'value': 0.6220640463718844,\n", + " 'value_error': 0.04980498364702598},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 30.8036961125628,\n", + " 'value_error': 16.481034493288803},\n", + " 'HadISST': {'value': 18.851909712101882,\n", + " 'value_error': 13.10078487406597},\n", + " 'Tropflux': {'value': 31.186003537248553,\n", + " 'value_error': 16.38997729081833}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MRI-CGCM3_r1i1p1': {'value': 1.0614112794596997,\n", + " 'value_error': 0.17023551986160482},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 48.13997063764262,\n", + " 'value_error': 24.821664550650453},\n", + " 'HadISST': {'value': 36.21275058240397,\n", + " 'value_error': 20.62972225465074},\n", + " 'Tropflux': {'value': 48.30486912698,\n", + " 'value_error': 24.742739508810562}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': 52.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 56.390977443609025,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 6.122448979591836, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16931799237617684,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1881117227188268, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1717269956444639, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-CGCM3_r1i1p1': {'value': -0.05084905436127311,\n", + " 'value_error': -0.004071182598802589},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 113.00550152470575,\n", + " 'value_error': -3.0976238207733235},\n", + " 'HadISST': {'value': 113.04159375158305,\n", + " 'value_error': -2.105473012960513},\n", + " 'Tropflux': {'value': 112.7939075725782,\n", + " 'value_error': -3.0472268049260913}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09485667347598477,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07643130873360211, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09399132165468681, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7623818692494935,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8790280257158316, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1141944466733926,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9368837502433505, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16826374678993009,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18251236774941054, 'value_error': None},\n", + " 'Tropflux': {'value': 0.16980270652911933, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.1241509642634093,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.6943190077151806, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r2i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3437647215320765,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3867375873753134, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.794064556711856,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.7662637865182262, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.9894914859394551,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.809666425352238, 'value_error': None},\n", + " 'Tropflux': {'value': 1.0376775260026352, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.754624382094368,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.478878001696872, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MRI-CGCM3_r2i1p1': {'value': 0.6251624507063739,\n", + " 'value_error': 0.05005305452993772},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 30.45904007731058,\n", + " 'value_error': 16.563123964636173},\n", + " 'HadISST': {'value': 18.44772368632206,\n", + " 'value_error': 13.166037847414563},\n", + " 'Tropflux': {'value': 30.8432517158609,\n", + " 'value_error': 16.471613220392207}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MRI-CGCM3_r2i1p1': {'value': 1.0890724138281442,\n", + " 'value_error': 0.174671978829302},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 46.788461313873356,\n", + " 'value_error': 25.468534818256344},\n", + " 'HadISST': {'value': 34.55040940393847,\n", + " 'value_error': 21.167347518591846},\n", + " 'Tropflux': {'value': 46.957657175359486,\n", + " 'value_error': 25.38755293357936}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': 54.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 62.40601503759399,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 10.204081632653061, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.18571532459477783,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.20319417652601632, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18781644836076505, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-CGCM3_r2i1p1': {'value': -0.026375388546153462,\n", + " 'value_error': -0.0021117211368936973},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 106.74594956111818,\n", + " 'value_error': -1.6067365041293806},\n", + " 'HadISST': {'value': 106.76467058787753,\n", + " 'value_error': -1.0921081913485464},\n", + " 'Tropflux': {'value': 106.63619584452537,\n", + " 'value_error': -1.5805955878186582}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13233313023704538,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.0955406916499014, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13120367269362737, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7232385775312604,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.828424451707454, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0868524025975985,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.8914744363913386, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17019173926043465,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1811451491092616, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17323404024390923, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.957395517396392,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5328135282287696, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r3i1p1': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.3862040436923455,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 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" 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1501152664186701,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16996554929497162, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15269095570231547, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-CGCM3_r3i1p1': {'value': 0.0061682427589008154,\n", + " 'value_error': 0.0004938546626021924},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 98.42236809291116,\n", + " 'value_error': 0.3757571491208629},\n", + " 'HadISST': {'value': 98.4179899265935,\n", + " 'value_error': 0.2554043301176068},\n", + " 'Tropflux': {'value': 98.44803548986538,\n", + " 'value_error': 0.3696437408780798}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.10682533017615777,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09540770395378151, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10954711000680102, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7255555784396224,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.84044266540961, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1530838130636665,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9819068524551378, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.173139488605604,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18695515328145088, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17488422741030912, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.0216210378755077,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5939097331113343, 'value_error': None}}}}},\n", + " 'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r4i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r4i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.363968360953125,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.433042123539411, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5904080299979357,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.575503722720228, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.886135014068642,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7060932128094128, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9342613895648904, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.265591602018585,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.948278144476324, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MRI-CGCM3_r4i1p2': {'value': 0.6407151621495945,\n", + " 'value_error': 0.0512982680149708},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 28.72900897589029,\n", + " 'value_error': 16.975179242954976},\n", + " 'HadISST': {'value': 16.418876592880505,\n", + " 'value_error': 13.493580852052785},\n", + " 'Tropflux': {'value': 29.12277898241481,\n", + " 'value_error': 16.881391906126836}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': 14.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 7.6923076923076925,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 7.6923076923076925, 'value_error': None},\n", + " 'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MRI-CGCM3_r4i1p2': {'value': 1.1851558451880786,\n", + " 'value_error': 0.19008241699232156},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 42.0938724509222,\n", + " 'value_error': 27.71549671535034},\n", + " 'HadISST': {'value': 28.77611821289851,\n", + " 'value_error': 23.03483709646617},\n", + " 'Tropflux': {'value': 42.27799562003,\n", + " 'value_error': 27.627370202585304}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': 51.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 53.383458646616546,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 4.081632653061225, 'value_error': None},\n", + " 'Tropflux': {'value': 59.375, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14173260082453912,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16171806728744534, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14439642152302176, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-CGCM3_r4i1p2': {'value': 0.27883245817319835,\n", + " 'value_error': 0.022324463374104216},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 28.6839056210487,\n", + " 'value_error': 16.985921867996286},\n", + " 'HadISST': {'value': 28.485992710620806,\n", + " 'value_error': 11.545430356483251},\n", + " 'Tropflux': {'value': 29.844188000876713,\n", + " 'value_error': 16.709568177848208}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09268721746397089,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.05447868371953464, 'value_error': None},\n", + " 'Tropflux': {'value': 0.08926163962540513, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7398976792055463,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8521981747552196, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0966930979347373,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9068186352583163, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16799816594381642,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1798018926295382, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17086637791072434, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.954017751026657,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.5343983582794904, 'value_error': None}}}}},\n", + " 'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-CGCM3_r5i1p2': {'keyerror': None,\n", + " 'name': 'MRI-CGCM3_r5i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-CGCM3_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.36677654737218,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.4322769677803815, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.5700676148953105,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.5578972371487463, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8849466243103553,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.7053106491687702, 'value_error': None},\n", + " 'Tropflux': {'value': 0.9330646412953516, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 10.42901803639116,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.12400661524261, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MRI-CGCM3_r5i1p2': {'value': 0.6294336227347035,\n", + " 'value_error': 0.05039502197567789},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 29.98392932408256,\n", + " 'value_error': 16.676284874571785},\n", + " 'HadISST': {'value': 17.890550425125685,\n", + " 'value_error': 13.25598952719678},\n", + " 'Tropflux': {'value': 30.370765934752818,\n", + " 'value_error': 16.58414891982347}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MRI-CGCM3_r5i1p2': {'value': 1.1268099333888544,\n", + " 'value_error': 0.18072454901112175},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 44.944624800776204,\n", + " 'value_error': 26.351046686781405},\n", + " 'HadISST': {'value': 32.28251135235024,\n", + " 'value_error': 21.900818664209588},\n", + " 'Tropflux': {'value': 45.11968347914333,\n", + " 'value_error': 26.267258693513064}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': 41.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 24.81203007518797,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.306122448979592, 'value_error': None},\n", + " 'Tropflux': {'value': 29.6875, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1498622088738023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16917228521706992, 'value_error': None},\n", + " 'Tropflux': {'value': 0.15231306218503973, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-CGCM3_r5i1p2': {'value': 0.09258039108999234,\n", + " 'value_error': 0.007412363551896706},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 76.32100670104572,\n", + " 'value_error': 5.639814316690287},\n", + " 'HadISST': {'value': 76.25529392582142,\n", + " 'value_error': 3.833414749159326},\n", + " 'Tropflux': {'value': 76.70625380320546,\n", + " 'value_error': 5.5480569478362645}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1289767064024115,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.09915640634915235, 'value_error': None},\n", + " 'Tropflux': {'value': 0.12900336682070224, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.759008617392554,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.8724449530149194, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1427387089427534,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.9682725272942712, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.17277307582884724,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18593581897843633, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17525807110111102, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.198535614584281,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.778947108263815, 'value_error': None}}}}}},\n", + " 'MRI-ESM1': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'MRI-ESM1_r1i1p1': {'keyerror': None,\n", + " 'name': 'MRI-ESM1_r1i1p1',\n", + " 'nyears': 155,\n", + " 'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"MRI-ESM1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.327604777192569,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 2.3958133096216483, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4011541632408986,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.433243405036624, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.6546879445654754,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.4917182302331993, 'value_error': None},\n", + " 'Tropflux': {'value': 0.7016656411076839, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.931273684078128,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.62531735330845, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'MRI-ESM1_r1i1p1': {'value': 0.616252641448402,\n", + " 'value_error': 0.04949860330985777},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 31.45013717187752,\n", + " 'value_error': 16.34474247316522},\n", + " 'HadISST': {'value': 19.610005947016514,\n", + " 'value_error': 12.999124643471335},\n", + " 'Tropflux': {'value': 31.828873029836235,\n", + " 'value_error': 16.254438279862743}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': 15.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 15.384615384615385,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 15.384615384615385, 'value_error': None},\n", + " 'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'MRI-ESM1_r1i1p1': {'value': 1.0763027298686678,\n", + " 'value_error': 0.1731816612352417},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 47.41238174688739,\n", + " 'value_error': 25.197160929402106},\n", + " 'HadISST': {'value': 35.3178244780666,\n", + " 'value_error': 20.952673910196072},\n", + " 'Tropflux': {'value': 47.57959373875108,\n", + " 'value_error': 25.11704192785242}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': 37.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 11.278195488721805,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 24.489795918367346, 'value_error': None},\n", + " 'Tropflux': {'value': 15.625, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.13008539479034462,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.15082415116644554, 'value_error': None},\n", + " 'Tropflux': {'value': 0.13289049859327376, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'MRI-ESM1_r1i1p1': {'value': 0.04011568159178435,\n", + " 'value_error': 0.0032221690846619354},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 89.73973922111108,\n", + " 'value_error': 2.446413650147492},\n", + " 'HadISST': {'value': 89.71126545105703,\n", + " 'value_error': 1.6636963890947793},\n", + " 'Tropflux': {'value': 89.90666927943603,\n", + " 'value_error': 2.406611545492774}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1139285804807429,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07888974529056147, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1131783157364191, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.7675116142737712,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.9001503863004432, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1779838660348614,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.0363883157052212, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1688418977100261,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.18297800684437796, 'value_error': None},\n", + " 'Tropflux': {'value': 0.17131187614313437, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.2612350613427394,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 2.8383530469973737, 'value_error': None}}}}}},\n", + " 'NorESM1-M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4612723944027584,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2776487049381702, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8368070324936655,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6470182608799538, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8420815666591608,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6149828803303297, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8897956320741629, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.47123439275413,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.833317897038764, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'NorESM1-M_r1i1p1': {'value': 0.9251779222960015,\n", + " 'value_error': 0.07407351631924254},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 2.91366786832411,\n", + " 'value_error': 24.511767459831816},\n", + " 'HadISST': {'value': 20.689215216215022,\n", + " 'value_error': 19.48441965248911},\n", + " 'Tropflux': {'value': 2.3450730573685106,\n", + " 'value_error': 24.37634070774245}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'NorESM1-M_r1i1p1': {'value': 1.621255937460463,\n", + " 'value_error': 0.26002677066215835},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 20.78623795726472,\n", + " 'value_error': 37.91392819085063},\n", + " 'HadISST': {'value': 2.567968841266154,\n", + " 'value_error': 31.510932982112383},\n", + " 'Tropflux': {'value': 21.03811266416682,\n", + " 'value_error': 37.79337389189653}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': 9.5, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 71.42857142857143,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 80.61224489795919, 'value_error': None},\n", + " 'Tropflux': {'value': 70.3125, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14392620928054034,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.13220367974645442, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14112413300378493, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'NorESM1-M_r1i1p1': {'value': -0.15161272637632747,\n", + " 'value_error': -0.012138732983998578},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 138.77750665809535,\n", + " 'value_error': -9.23594742625892},\n", + " 'HadISST': {'value': 138.88512008348934,\n", + " 'value_error': -6.277727438916557},\n", + " 'Tropflux': {'value': 138.14661319567472,\n", + " 'value_error': -9.085682508458408}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1048454518123474,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.10368462869282168, 'value_error': None},\n", + " 'Tropflux': {'value': 0.10526134914561627, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1882023863249815,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.412612606880112, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2804028213499271,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4372637724336723, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1735161484583269,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16945068656518608, 'value_error': None},\n", + " 'Tropflux': {'value': 0.18455988328788184, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.845294223233808,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.859575691574018, 'value_error': None}}}}},\n", + " 'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r2i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r2i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5089821722592016,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.2973174480893828, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.7732326935836333,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5873658998732889, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8387109874122113,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.6122316232440536, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8861332471990906, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.789427290643365,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 10.156360777661654, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'NorESM1-M_r2i1p1': {'value': 0.8234132714447572,\n", + " 'value_error': 0.06592582348752801},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 8.406288246117409,\n", + " 'value_error': 21.815603406212507},\n", + " 'HadISST': {'value': 7.414043433571324,\n", + " 'value_error': 17.34123711949705},\n", + " 'Tropflux': {'value': 8.912340651965538,\n", + " 'value_error': 21.695072876579562}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'NorESM1-M_r2i1p1': {'value': 1.6372868769735383,\n", + " 'value_error': 0.2625979090838901},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 20.002974193309374,\n", + " 'value_error': 38.28882019617008},\n", + " 'HadISST': {'value': 1.604563272748492,\n", + " 'value_error': 31.82251232561078},\n", + " 'Tropflux': {'value': 20.25733943122384,\n", + " 'value_error': 38.16707385922252}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': 12.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1433270602553856,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.1286950346777803, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1404974965712768, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'NorESM1-M_r2i1p1': {'value': 0.006002423136812571,\n", + " 'value_error': 0.0004805784676273684},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 98.46477925227259,\n", + " 'value_error': 0.36565574570669557},\n", + " 'HadISST': {'value': 98.46051878331424,\n", + " 'value_error': 0.2485383471861333},\n", + " 'Tropflux': {'value': 98.48975663778769,\n", + " 'value_error': 0.3597066829807976}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.09772163490195734,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.07971554175641021, 'value_error': None},\n", + " 'Tropflux': {'value': 0.09376265504540192, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2382088330136434,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.4514777102138254, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.3457750986688298,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.47952080886657833, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1517307194091035,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14813681101122989, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1628756763465747, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.9219590854779076,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.9396561427316157, 'value_error': None}}}}},\n", + " 'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-M_r3i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-M_r3i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.5081003853452086,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3256931626518191, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8145372929519737,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.6660464870504684, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8192331184544785,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.5923284535054455, 'value_error': None},\n", + " 'Tropflux': {'value': 0.8667393513949748, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.613054431371344,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.966888334745294, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'NorESM1-M_r3i1p1': {'value': 0.8894687928431987,\n", + " 'value_error': 0.07121449783261045},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0584951068284376,\n", + " 'value_error': 23.56568578597612},\n", + " 'HadISST': {'value': 16.030968725618212,\n", + " 'value_error': 18.732378724019068},\n", + " 'Tropflux': {'value': 1.6051438410118877,\n", + " 'value_error': 23.43548610568039}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'NorESM1-M_r3i1p1': {'value': 1.5448679750461873,\n", + " 'value_error': 0.2477752101743308},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 24.5185159633469,\n", + " 'value_error': 36.12755525940786},\n", + " 'HadISST': {'value': 7.1586285650837524,\n", + " 'value_error': 30.02624699968399},\n", + " 'Tropflux': {'value': 24.75852320676012,\n", + " 'value_error': 36.01268106132162}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': 12.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 63.90977443609023,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 75.51020408163265, 'value_error': None},\n", + " 'Tropflux': {'value': 62.5, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.1469869475923206,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.12705519427161696, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1436117115226541, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'NorESM1-M_r3i1p1': {'value': 0.06671481487310944,\n", + " 'value_error': 0.005341460068539592},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 82.93656317798705,\n", + " 'value_error': 4.0641345713366395},\n", + " 'HadISST': {'value': 82.88920956906111,\n", + " 'value_error': 2.762416018241005},\n", + " 'Tropflux': {'value': 83.21417800331228,\n", + " 'value_error': 3.9980128385996405}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11332559172934832,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.08953936739580493, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11115623812858066, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.2140436269239625,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.437623506510688, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.2735712132148087,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.4550980740765266, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.16430931476137625,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.16101268046078984, 'value_error': None},\n", + " 'Tropflux': {'value': 0.1753812621771339, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 3.919635820684615,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 3.929618877413432, 'value_error': None}}}}}},\n", + " 'NorESM1-ME': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Meridional RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstDiversity',\n", + " 'units': '%'}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO skewness',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSstSkew',\n", + " 'units': '%'}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstTsRmse',\n", + " 'units': 'C/C'}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr meridional seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLatRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'GPCPv2.3': {'name': 'GPCPv2.3',\n", + " 'nyears': 41,\n", + " 'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'pr zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'mm/day'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalPrLonRmse',\n", + " 'units': 'mm/day'}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p1': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p1',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'taux zonal seasonality RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': '1e-3 N/m2'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'SeasonalTauxLonRmse',\n", + " 'units': '1e-3 N/m2'}}},\n", + " 'name': 'Metrics Collection for ENSO performance'},\n", + " 'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.4256576958565477,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.1487609443249653, 'value_error': None}}},\n", + " 'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.8849899564213228,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5949633386553073, 'value_error': None}}},\n", + " 'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.0467551796522485,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.8215385339256356, 'value_error': None},\n", + " 'Tropflux': {'value': 1.09404035574318, 'value_error': None}}},\n", + " 'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 9.495500903052976,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 9.956511088257153, 'value_error': None}}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,\n", + " 'value_error': 0.14214193002531866},\n", + " 'HadISST': {'value': 0.7665787872085695,\n", + " 'value_error': 0.06238329697691264},\n", + " 'NorESM1-ME_r1i1p1': {'value': 0.8766922107094253,\n", + " 'value_error': 0.07019155257810035},\n", + " 'Tropflux': {'value': 0.9039789553693535,\n", + " 'value_error': 0.14293162279134272}},\n", + " 'metric': {'ERA-Interim': {'value': 2.4797189585005452,\n", + " 'value_error': 23.227181588408055},\n", + " 'HadISST': {'value': 14.364266966194597,\n", + " 'value_error': 18.463301520575715},\n", + " 'Tropflux': {'value': 3.0185154751505476,\n", + " 'value_error': 23.098852133265225}}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 13.0, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': 13.0, 'value_error': None},\n", + " 'Tropflux': {'value': 13.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},\n", + " 'HadISST': {'value': 0.0, 'value_error': None},\n", + " 'Tropflux': {'value': 0.0, 'value_error': None}}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,\n", + " 'value_error': 0.6513411033995022},\n", + " 'HadISST': {'value': 1.6639865947361325,\n", + " 'value_error': 0.2712772451657809},\n", + " 'NorESM1-ME_r1i1p1': {'value': 1.5875394467486952,\n", + " 'value_error': 0.25461911725268216},\n", + " 'Tropflux': {'value': 2.0532132553583624,\n", + " 'value_error': 0.6534187683977347}},\n", + " 'metric': {'ERA-Interim': {'value': 22.433608992550873,\n", + " 'value_error': 37.12545021019583},\n", + " 'HadISST': {'value': 4.594216577781986,\n", + " 'value_error': 30.855615055644385},\n", + " 'Tropflux': {'value': 22.680245580646698,\n", + " 'value_error': 37.007403021813865}}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 49.0, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': 10.0, 'value_error': None},\n", + " 'Tropflux': {'value': 32.0, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 69.92481203007519,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 79.59183673469387, 'value_error': None},\n", + " 'Tropflux': {'value': 68.75, 'value_error': None}}},\n", + " 'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.14520503132540324,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.14432609308703653, 'value_error': None},\n", + " 'Tropflux': {'value': 0.14357545853464462, 'value_error': None}}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,\n", + " 'value_error': 0.061819541653923164},\n", + " 'HadISST': {'value': 0.38989908235027515,\n", + " 'value_error': 0.03172953733021198},\n", + " 'NorESM1-ME_r1i1p1': {'value': 0.16181339203161868,\n", + " 'value_error': 0.012955439863480947},\n", + " 'Tropflux': {'value': 0.39744741059612115,\n", + " 'value_error': 0.06284195338099416}},\n", + " 'metric': {'ERA-Interim': {'value': 58.61350140686601,\n", + " 'value_error': 9.857351802770573},\n", + " 'HadISST': {'value': 58.49864763563364,\n", + " 'value_error': 6.700099625011838},\n", + " 'Tropflux': {'value': 59.28684205316147,\n", + " 'value_error': 9.696976901310798}}},\n", + " 'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.07966539173883842,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.06885955355179388, 'value_error': None},\n", + " 'Tropflux': {'value': 0.07851343494412877, 'value_error': None}}},\n", + " 'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 1.1175939248109552,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 1.3287493804780544, 'value_error': None}}},\n", + " 'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.4288091464874073,\n", + " 'value_error': None},\n", + " 'GPCPv2.3': {'value': 0.5846776538992703, 'value_error': None}}},\n", + " 'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': None, 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 0.11147684214687681,\n", + " 'value_error': None},\n", + " 'HadISST': {'value': 0.11975436226428014, 'value_error': None},\n", + " 'Tropflux': {'value': 0.11892174199206576, 'value_error': None}}},\n", + " 'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,\n", + " 'value_error': None},\n", + " 'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},\n", + " 'Tropflux': {'value': None, 'value_error': None}},\n", + " 'metric': {'ERA-Interim': {'value': 4.274361614689152,\n", + " 'value_error': None},\n", + " 'Tropflux': {'value': 4.248843544142059, 'value_error': None}}}}},\n", + " 'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',\n", + " 'metrics': {'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',\n", + " 'name': 'sst Zonal RMSE',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'BiasSstLonRmse',\n", + " 'units': 'C'}},\n", + " 'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO amplitude',\n", + " 'ref': 'Using CDAT regression calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoAmpl',\n", + " 'units': '%'}},\n", + " 'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Duration based on life cyle SSTA pattern',\n", + " 'ref': 'Using CDAT',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'months'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoDuration',\n", + " 'units': '%'}},\n", + " 'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',\n", + " 'name': 'ENSO seasonality',\n", + " 'ref': 'Using CDAT std dev calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',\n", + " 'name': 'EnsoSeasonality',\n", + " 'units': '%'}},\n", + " 'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 156,\n", + " 'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},\n", + " 'Tropflux': {'keyerror': None,\n", + " 'name': 'Tropflux',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},\n", + " 'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',\n", + " 'name': 'ENSO Diversity (interquartile range)',\n", + " 'ref': 'Using CDAT regridding',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'long'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's 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shaped window of 5 points, observations and model regridded to generic_1x1deg',\n", + " 'name': 'ENSO Zonal SSTA pattern',\n", + " 'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',\n", + " 'time_frequency': 'monthly',\n", + " 'units': 'C/C'},\n", + " 'metric': {'datasets': \"NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's \",\n", + " 'method': 'The metric is the statistical value between the model and the observations',\n", + " 'name': 'EnsoSstLonRmse',\n", + " 'units': 'C/C'}},\n", + " 'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,\n", + " 'name': 'ERA-Interim',\n", + " 'nyears': 40,\n", + " 'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},\n", + " 'HadISST': {'keyerror': None,\n", + " 'name': 'HadISST',\n", + " 'nyears': 151,\n", + " 'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},\n", + " 'NorESM1-ME_r1i1p2': {'keyerror': None,\n", + " 'name': 'NorESM1-ME_r1i1p2',\n", + " 'nyears': 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None,\n", + " 'varname': 'pr'},\n", + " 'sst': {'areaname': None,\n", + " 'landmaskname': 'lsmask',\n", + " 'path + filename': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/ts/ERA-INT/gn/v20200707/ts_mon_ERA-INT_BE_gn_v20200707_197901-201903.nc',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': None,\n", + " 'varname': 'ts'},\n", + " 'taux': {'areaname': None,\n", + " 'landmaskname': 'lsmask',\n", + " 'path + filename': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/tauu/ERA-INT/gn/v20200707/tauu_mon_ERA-INT_BE_gn_v20200707_197901-201903.nc',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': None,\n", + " 'varname': 'tauu'}},\n", + " 'GPCPv2.3': {'pr': {'areaname': None,\n", + " 'landmaskname': 'lsmask',\n", + " 'path + filename': '/p/user_pub/pmp/pmp_obs_preparation/orig/data/GPCP_v2.3_mon_jwl/precip.mon.mean.nc',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': '/work/lee1043/DATA/GPCP/gpcp_25_lsmask.nc',\n", + " 'varname': 'precip'}},\n", + " 'HadISST': {'sst': {'areaname': None,\n", + " 'landmaskname': None,\n", + " 'path + filename': '/work/lee1043/DATA/HadISSTv1.1/HadISST_sst.nc',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': None,\n", + " 'varname': 'sst'}},\n", + " 'Tropflux': {'sst': {'areaname': None,\n", + " 'landmaskname': None,\n", + " 'path + filename': '/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_sst_mo.xml',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': None,\n", + " 'varname': 'sst'},\n", + " 'taux': {'areaname': None,\n", + " 'landmaskname': None,\n", + " 'path + filename': '/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_taux_mo.xml',\n", + " 'path + filename_area': None,\n", + " 'path + filename_landmask': None,\n", + " 'varname': 'taux'}}}},\n", + " 'YAML': 'name: pmp_nightly_20201021\\nchannels:\\n - pcmdi/label/nightly\\n - cdat/label/nightly\\n - conda-forge\\n - defaults\\ndependencies:\\n - _libgcc_mutex=0.1=main\\n - argon2-cffi=20.1.0=py37h7b6447c_1\\n - async_generator=1.10=py37h28b3542_0\\n - attrs=20.3.0=pyhd3eb1b0_0\\n - backcall=0.2.0=py_0\\n - basemap=1.2.1=py37hd1be537_2\\n - bleach=3.2.1=py_0\\n - bokeh=2.2.3=py37_0\\n - brotlipy=0.7.0=py37h27cfd23_1003\\n - bzip2=1.0.8=h7b6447c_0\\n - ca-certificates=2020.11.8=ha878542_0\\n - cdat_info=8.2.2020.08.27.15.53.ga42e5c8=pyh9f0ad1d_0\\n - cdms2=3.1.4.2020.08.28.18.50.gb7d81f3=py37h46565e8_0\\n - cdp=1.6.0=py_0\\n - cdtime=3.1.4.2020.10.12.15.52.g2b715b5=py37hd741776_0\\n - cdutil=8.2.2020.09.28.17.09.g484910c=pyh9f0ad1d_0\\n - certifi=2020.11.8=py37h89c1867_0\\n - cffi=1.14.0=py37h2e261b9_0\\n - cftime=1.3.0=py37ha21ca33_0\\n - chardet=3.0.4=py37h06a4308_1003\\n - cia=0.0.6=0\\n - click=7.1.2=py_0\\n - cloudpickle=1.6.0=py_0\\n - cmocean=2.0=py_3\\n - colorspacious=1.1.2=pyh24bf2e0_0\\n - cryptography=3.2.1=py37h3c74f83_1\\n - curl=7.71.1=hbc83047_1\\n - cycler=0.10.0=py37_0\\n - cytoolz=0.11.0=py37h7b6447c_0\\n - dask=2.30.0=py_0\\n - dask-core=2.30.0=py_0\\n - decorator=4.4.2=py_0\\n - defusedxml=0.6.0=py_0\\n - distarray=2.12.2=py_1\\n - distributed=2.30.1=py37h06a4308_0\\n - dv3d=8.2.2020.07.17.21.42.g86f50aa=pyh9f0ad1d_0\\n - entrypoints=0.3=py37_0\\n - eofs=1.4.0=py_0\\n - esmf=8.0.1=nompi_hbeb3ca6_1\\n - esmpy=8.0.1=nompi_py37h59b2dc9_2\\n - expat=2.2.10=he6710b0_2\\n - ffmpeg=4.2.3=h167e202_0\\n - freetype=2.10.4=h5ab3b9f_0\\n - fsspec=0.8.3=py_0\\n - future=0.18.2=py37_1\\n - g2clib=1.6.0=h838ce51_4\\n - genutil=8.2.2020.10.07.17.47.ge34ccd5=py37h161383b_0\\n - geos=3.8.0=he1b5a44_1\\n - ghostscript=9.53.3=he1b5a44_1\\n - gmp=6.2.0=he1b5a44_3\\n - gnutls=3.6.5=h71b1129_1002\\n - hdf4=4.2.13=h3ca952b_2\\n - hdf5=1.10.6=nompi_h54c07f9_1110\\n - heapdict=1.0.1=py_0\\n - idna=2.10=py_0\\n - importlib-metadata=2.0.0=py_1\\n - importlib_metadata=2.0.0=1\\n - ipykernel=5.3.4=py37h5ca1d4c_0\\n - ipython=7.19.0=py37hb070fc8_0\\n - ipython_genutils=0.2.0=py37_0\\n - jasper=1.900.1=hd497a04_4\\n - jedi=0.17.2=py37_0\\n - jinja2=2.11.2=py_0\\n - jpeg=9d=h516909a_0\\n - json5=0.9.5=py_0\\n - jsonschema=3.2.0=py_2\\n - jupyter_client=6.1.7=py_0\\n - jupyter_core=4.6.3=py37_0\\n - jupyterlab=2.2.6=py_0\\n - jupyterlab_pygments=0.1.2=py_0\\n - jupyterlab_server=1.2.0=py_0\\n - kiwisolver=1.3.0=py37h2531618_0\\n - krb5=1.18.2=h173b8e3_0\\n - lame=3.100=h7b6447c_0\\n - lazy-object-proxy=1.5.1=py37h7b6447c_0\\n - lcms2=2.11=h396b838_0\\n - ld_impl_linux-64=2.33.1=h53a641e_7\\n - libblas=3.8.0=17_openblas\\n - libcblas=3.8.0=17_openblas\\n - libcdms=3.1.2=h054cd8a_112\\n - libcf=1.0.3=py37hda0e254_109\\n - libcurl=7.71.1=h20c2e04_1\\n - libdrs=3.1.2=hc2e2db3_113\\n - libdrs_f=3.1.2=hae7e664_110\\n - libedit=3.1.20191231=h14c3975_1\\n - libffi=3.2.1=hf484d3e_1007\\n - libgcc-ng=9.1.0=hdf63c60_0\\n - libgfortran-ng=7.5.0=hae1eefd_17\\n - libgfortran4=7.5.0=hae1eefd_17\\n - libiconv=1.15=h63c8f33_5\\n - liblapack=3.8.0=17_openblas\\n - libnetcdf=4.7.4=nompi_hefab0ff_106\\n - libopenblas=0.3.10=pthreads_hb3c22a3_5\\n - libpng=1.6.37=hbc83047_0\\n - libsodium=1.0.18=h7b6447c_0\\n - libssh2=1.9.0=h1ba5d50_1\\n - libstdcxx-ng=9.1.0=hdf63c60_0\\n - libtiff=4.1.0=h2733197_1\\n - libuuid=2.32.1=h14c3975_1000\\n - locket=0.2.0=py37_1\\n - lz4-c=1.9.2=heb0550a_3\\n - markupsafe=1.1.1=py37h14c3975_1\\n - matplotlib=3.3.2=0\\n - matplotlib-base=3.3.2=py37h817c723_0\\n - mesalib=18.3.1=h590aaf7_0\\n - mistune=0.8.4=py37h14c3975_1001\\n - msgpack-python=1.0.0=py37hfd86e86_1\\n - nb_conda=2.2.1=py37_0\\n - nb_conda_kernels=2.3.0=py37_0\\n - nbclient=0.5.1=py_0\\n - nbconvert=6.0.7=py37_0\\n - nbformat=5.0.8=py_0\\n - ncurses=6.2=he6710b0_1\\n - nest-asyncio=1.4.2=pyhd3eb1b0_0\\n - netcdf-fortran=4.5.3=nompi_hfef6a68_101\\n - netcdf4=1.5.4=nompi_py37hcbfd489_103\\n - nettle=3.4.1=hbb512f6_0\\n - notebook=6.1.4=py37_0\\n - numpy=1.19.2=py37h7008fea_1\\n - olefile=0.46=py37_0\\n - openblas=0.3.10=pthreads_h43bd3aa_5\\n - openh264=2.1.1=h8b12597_0\\n - openssl=1.1.1h=h516909a_0\\n - output_viewer=1.3.1=py_1\\n - packaging=20.4=py_0\\n - pandas=1.1.3=py37he6710b0_0\\n - pandoc=2.11=hb0f4dca_0\\n - pandocfilters=1.4.3=py37h06a4308_1\\n - parso=0.7.0=py_0\\n - partd=1.1.0=py_0\\n - pcmdi_metrics=1.2.2020.07.22.22.42.gf7c21da=pyh95af2a2_0\\n - pexpect=4.8.0=pyhd3eb1b0_3\\n - pickleshare=0.7.5=py37_1001\\n - pillow=8.0.1=py37he98fc37_0\\n - pip=20.2.4=py37h06a4308_0\\n - proj4=5.2.0=he6710b0_1\\n - prometheus_client=0.8.0=py_0\\n - prompt-toolkit=3.0.8=py_0\\n - psutil=5.7.2=py37h7b6447c_0\\n - ptyprocess=0.6.0=pyhd3eb1b0_2\\n - pycparser=2.20=py_2\\n - pygments=2.7.2=pyhd3eb1b0_0\\n - pyopenssl=19.1.0=pyhd3eb1b0_1\\n - pyparsing=2.4.7=py_0\\n - pyproj=1.9.6=py37h516909a_1002\\n - pyrsistent=0.17.3=py37h7b6447c_0\\n - pyshp=2.1.2=pyh9f0ad1d_0\\n - pysocks=1.7.1=py37_1\\n - python=3.7.7=hcf32534_0_cpython\\n - python-dateutil=2.8.1=py_0\\n - python_abi=3.7=1_cp37m\\n - pytz=2020.1=py_0\\n - pyyaml=5.3.1=py37h7b6447c_1\\n - pyzmq=19.0.2=py37he6710b0_1\\n - readline=8.0=h7b6447c_0\\n - requests=2.24.0=py_0\\n - scipy=1.5.2=py37hb14ef9d_2\\n - send2trash=1.5.0=py37_0\\n - setuptools=50.3.1=py37h06a4308_1\\n - six=1.15.0=py37h06a4308_0\\n - sortedcontainers=2.2.2=py_0\\n - sqlite=3.33.0=h62c20be_0\\n - tblib=1.7.0=py_0\\n - terminado=0.9.1=py37_0\\n - testpath=0.4.4=py_0\\n - tk=8.6.10=hbc83047_0\\n - toolz=0.11.1=py_0\\n - tornado=6.0.4=py37h7b6447c_1\\n - traitlets=5.0.5=py_0\\n - typing_extensions=3.7.4.3=py_0\\n - udunits2=2.2.25=hd30922c_1\\n - urllib3=1.25.11=py_0\\n - vcs=8.2.2020.08.06.20.48.g4abe712=pyh9f0ad1d_0\\n - vcsaddons=8.2.2020.07.22.18.33.g40b269e=py37h8f50634_0\\n - vtk-cdat=8.2.0.8.2.2020.07.20.18.56.g3aa9eaf=py37_mesalibh34e701b_0\\n - wcwidth=0.2.5=py_0\\n - webencodings=0.5.1=py37_1\\n - wheel=0.35.1=pyhd3eb1b0_0\\n - x264=1!152.20180806=h7b6447c_0\\n - xarray=0.16.1=py_0\\n - xz=5.2.5=h7b6447c_0\\n - yaml=0.2.5=h7b6447c_0\\n - zeromq=4.3.3=he6710b0_3\\n - zict=2.0.0=py_0\\n - zipp=3.4.0=pyhd3eb1b0_0\\n - zlib=1.2.11=h7b6447c_3\\n - zstd=1.4.5=h9ceee32_0\\n - pip:\\n - ensometrics==1.0-2020\\nprefix: /export/lee1043/anaconda3/envs/pmp_nightly_20201021\\n\\n',\n", + " 'json_structure': ['type', 'data', 'metric', 'item', 'value or description'],\n", + " 'json_version': 3.0,\n", + " 'provenance': {'commandLine': 'PMPdriver_EnsoMetrics.py -p ./my_Param_ENSO.py --mip cmip5 --metricsCollection ENSO_perf --case_id v20210104 --modnames NorESM1-M --realization r3i1p1',\n", + " 'conda': {'Platform': 'linux-64',\n", + " 'PythonVersion': '3.7.3.final.0',\n", + " 'Version': '4.8.3',\n", + " 'buildVersion': '3.18.8'},\n", + " 'date': '2021-01-04 23:16:29',\n", + " 'history': 'import EnsoMetrics\\nfrom ...script.PMPdriver_lib import AddParserArgument\\nfrom ...script.PMPdriver_lib import AddParserArgument\\nfrom script.PMPdriver_lib import AddParserArgument\\nfrom script.PMPdriver_libfrom PMPdriver_lib import AddParserArgument\\n import AddParserArgument\\nfrom PMPdriver_lib import AddParserArgument\\n',\n", + " 'openGL': {'GLX': {'client': {}, 'server': {}}},\n", + " 'osAccess': False,\n", + " 'packages': {'PMP': 'v1.2.1-404-g9652ad1',\n", + " 'PMPObs': \"See 'References' key below, for detailed obs provenance information.\",\n", + " 'blas': '0.3.10',\n", + " 'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',\n", + " 'cdms': '3.1.4.2020.08.28.18.50.gb7d81f3',\n", + " 'cdp': '1.6.0',\n", + " 'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',\n", + " 'cdutil': '8.2.2020.09.28.17.09.g484910c',\n", + " 'clapack': None,\n", + " 'esmf': '8.0.1',\n", + " 'esmpy': '8.0.1',\n", + " 'genutil': '8.2.2020.10.07.17.47.ge34ccd5',\n", + " 'lapack': '3.8.0',\n", + " 'matplotlib': '3.3.2',\n", + " 'mesalib': '18.3.1',\n", + " 'numpy': '1.19.2',\n", + " 'python': '3.7.7',\n", + " 'scipy': '1.5.2',\n", + " 'uvcdat': None,\n", + " 'vcs': '8.2.2020.08.06.20.48.g4abe712',\n", + " 'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},\n", + " 'platform': {'Name': 'gates.llnl.gov',\n", + " 'OS': 'Linux',\n", + " 'Version': '3.10.0-1127.19.1.el7.x86_64'},\n", + " 'script': '#!/usr/bin/env python\\n# =================================================\\n# Dependencies\\n# -------------------------------------------------\\nfrom __future__ import print_function\\n\\nimport cdms2\\nimport glob\\nimport json\\nimport os\\nimport pkg_resources\\nimport sys\\n\\nfrom genutil import StringConstructor\\nfrom PMPdriver_lib import AddParserArgument\\nfrom PMPdriver_lib import metrics_to_json\\nfrom PMPdriver_lib import sort_human\\nfrom PMPdriver_lib import find_realm, get_file\\nfrom EnsoMetrics.EnsoCollectionsLib import CmipVariables, defCollection, ReferenceObservations\\nfrom EnsoMetrics.EnsoComputeMetricsLib import ComputeCollection\\n\\n# To avoid below error when using multi cores\\n# OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable\\nos.environ[\\'OPENBLAS_NUM_THREADS\\'] = \\'1\\'\\n\\n# =================================================\\n# Collect user defined options\\n# -------------------------------------------------\\nparam = AddParserArgument()\\n\\n# Pre-defined options\\nmip = param.mip\\nexp = param.exp\\nprint(\\'mip:\\', mip)\\nprint(\\'exp:\\', exp)\\n\\n# Path to model data as string template\\nmodpath = param.process_templated_argument(\"modpath\")\\nmodpath_lf = param.process_templated_argument(\"modpath_lf\")\\n\\n# Check given model option\\nmodels = param.modnames\\n\\n# Include all models if conditioned\\nif (\\'all\\' in [m.lower() for m in models]) or (models == \\'all\\'):\\n model_index_path = param.modpath.split(\\'/\\')[-1].split(\\'.\\').index(\"%(model)\")\\n models = ([p.split(\\'/\\')[-1].split(\\'.\\')[model_index_path] for p in glob.glob(modpath(\\n mip=mip, exp=exp, model=\\'*\\', realization=\\'*\\', variable=\\'ts\\'))])\\n # remove duplicates\\n models = sorted(list(dict.fromkeys(models)), key=lambda s: s.lower())\\n\\nprint(\\'models:\\', models)\\n\\n# Realizations\\nrealization = param.realization\\nprint(\\'realization: \\', realization)\\n\\n# Metrics Collection\\nmc_name = param.metricsCollection \\ndict_mc = defCollection(mc_name)\\nlist_metric = sorted(dict_mc[\\'metrics_list\\'].keys())\\nprint(\\'mc_name:\\', mc_name)\\n\\n# case id\\ncase_id = param.case_id\\n\\n# Output\\noutdir_template = param.process_templated_argument(\"results_dir\")\\noutdir = StringConstructor(str(outdir_template(\\n output_type=\\'%(output_type)\\',\\n mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id)))\\nnetcdf_path = outdir(output_type=\\'diagnostic_results\\')\\njson_name_template = param.process_templated_argument(\"json_name\")\\nnetcdf_name_template = param.process_templated_argument(\"netcdf_name\")\\n\\nprint(\\'outdir:\\', str(outdir_template(\\n output_type=\\'%(output_type)\\',\\n mip=mip, exp=exp, metricsCollection=mc_name))) \\nprint(\\'netcdf_path:\\', netcdf_path)\\n\\n# Switches\\ndebug = param.debug\\nprint(\\'debug:\\', debug)\\n\\n# =================================================\\n# Prepare loop iteration\\n# -------------------------------------------------\\n# Environmental setup\\ntry:\\n egg_pth = pkg_resources.resource_filename(\\n pkg_resources.Requirement.parse(\"pcmdi_metrics\"), \"share/pmp\")\\nexcept Exception:\\n egg_pth = os.path.join(sys.prefix, \"share\", \"pmp\")\\nprint(\\'egg_pth:\\', egg_pth)\\n\\n# Create output directory\\nfor output_type in [\\'graphics\\', \\'diagnostic_results\\', \\'metrics_results\\']:\\n if not os.path.exists(outdir(output_type=output_type)):\\n os.makedirs(outdir(output_type=output_type))\\n print(outdir(output_type=output_type))\\n\\n# list of variables\\nlist_variables = list()\\nfor metric in list_metric:\\n listvar = dict_mc[\\'metrics_list\\'][metric][\\'variables\\']\\n for var in listvar:\\n if var not in list_variables:\\n list_variables.append(var)\\nlist_variables = sorted(list_variables)\\nprint(list_variables)\\n\\n# list of observations\\nlist_obs = list()\\nfor metric in list_metric:\\n dict_var_obs = dict_mc[\\'metrics_list\\'][metric][\\'obs_name\\']\\n for var in dict_var_obs.keys():\\n for obs in dict_var_obs[var]:\\n if obs not in list_obs:\\n list_obs.append(obs)\\nlist_obs = sorted(list_obs)\\n\\n#\\n# finding file and variable name in file for each observations dataset\\n#\\ndict_obs = dict()\\n\\nfor obs in list_obs:\\n # be sure to add your datasets to EnsoCollectionsLib.ReferenceObservations if needed\\n dict_var = ReferenceObservations(obs)[\\'variable_name_in_file\\']\\n dict_obs[obs] = dict()\\n for var in list_variables:\\n #\\n # finding variable name in file\\n #\\n try: var_in_file = dict_var[var][\\'var_name\\']\\n except:\\n print(\\'\\\\033[95m\\' + str(var) + \" is not available for \" + str(obs) + \" or unscripted\" + \\'\\\\033[0m\\')\\n else:\\n if isinstance(var_in_file, list):\\n var0 = var_in_file[0]\\n else:\\n var0 = var_in_file\\n\\n try:\\n # finding file for \\'obs\\', \\'var\\'\\n file_name = param.reference_data_path[obs].replace(\\'VAR\\', var0)\\n file_areacell = None ## temporary for now\\n try:\\n file_landmask = param.reference_data_lf_path[obs]\\n except:\\n file_landmask = None\\n try:\\n areacell_in_file = dict_var[\\'areacell\\'][\\'var_name\\']\\n except:\\n areacell_in_file = None\\n try:\\n landmask_in_file = dict_var[\\'landmask\\'][\\'var_name\\']\\n except:\\n landmask_in_file = None\\n # if var_in_file is a list (like for thf) all variables should be read from the same realm\\n if isinstance(var_in_file, list):\\n list_files = list()\\n list_files = [param.reference_data_path[obs].replace(\\'VAR\\', var1) for var1 in var_in_file]\\n list_areacell = [file_areacell for var1 in var_in_file]\\n list_name_area = [areacell_in_file for var1 in var_in_file]\\n try:\\n list_landmask = [param.reference_data_lf_path[obs] for var1 in var_in_file]\\n except:\\n list_landmask = None\\n list_name_land = [landmask_in_file for var1 in var_in_file]\\n else:\\n list_files = file_name\\n list_areacell = file_areacell\\n list_name_area = areacell_in_file\\n list_landmask = file_landmask\\n list_name_land = landmask_in_file\\n dict_obs[obs][var] = {\\'path + filename\\': list_files, \\'varname\\': var_in_file,\\n \\'path + filename_area\\': list_areacell, \\'areaname\\': list_name_area,\\n \\'path + filename_landmask\\': list_landmask, \\'landmaskname\\': list_name_land}\\n except:\\n print(\\'\\\\033[95m\\' + \\'Observation dataset\\' + str(obs) + \" is not given for variable \" + str(var) + \\'\\\\033[0m\\')\\n\\nprint(\\'PMPdriver: dict_obs readin end\\')\\n\\n# =================================================\\n# Loop for Models \\n# -------------------------------------------------\\n# finding file and variable name in file for each observations dataset\\ndict_metric, dict_dive = dict(), dict()\\ndict_var = CmipVariables()[\\'variable_name_in_file\\']\\n\\nprint(\\'models:\\', models)\\n\\nfor mod in models:\\n print(\\' ----- model: \\', mod, \\' ---------------------\\')\\n print(\\'PMPdriver: var loop start for model \\', mod)\\n dict_mod = {mod: {}}\\n dict_metric[mod], dict_dive[mod] = dict(), dict()\\n\\n model_path_list = glob.glob(\\n modpath(mip=mip, exp=exp, realm=\\'atmos\\', model=mod, realization=\\'*\\', variable=\\'ts\\'))\\n\\n model_path_list = sort_human(model_path_list)\\n if debug:\\n print(\\'model_path_list:\\', model_path_list)\\n\\n # Find where run can be gripped from given filename template for modpath\\n print(\\'realization:\\', realization)\\n run_in_modpath = modpath(mip=mip, exp=exp, realm=\\'atmos\\', model=mod, realization=realization,\\n variable=\\'ts\\').split(\\'/\\')[-1].split(\\'.\\').index(realization)\\n print(\\'run_in_modpath:\\', run_in_modpath)\\n # Collect all available runs\\n runs_list = [model_path.split(\\'/\\')[-1].split(\\'.\\')[run_in_modpath] for model_path in model_path_list]\\n\\n # Adjust realization to be included\\n if realization in [\"all\" ,\"*\"]:\\n pass\\n elif realization in [\"first\"]:\\n runs_list = runs_list[:1]\\n else:\\n runs_list = [realization]\\n\\n if debug:\\n print(\\'runs_list:\\', runs_list)\\n\\n # =================================================\\n # Loop for Realizations\\n # -------------------------------------------------\\n for run in runs_list:\\n\\n print(\\' --- run: \\', run, \\' ---\\')\\n mod_run = \\'_\\'.join([mod, run])\\n dict_mod = {mod_run: {}}\\n\\n if debug:\\n print(\\'list_variables:\\', list_variables)\\n \\n try:\\n for var in list_variables:\\n print(\\' --- var: \\', var, \\' ---\\')\\n # finding variable name in file\\n var_in_file = dict_var[var][\\'var_name\\']\\n print(\\'var_in_file:\\', var_in_file)\\n if isinstance(var_in_file, list):\\n var0 = var_in_file[0]\\n else:\\n var0 = var_in_file\\n # finding variable type (atmos or ocean)\\n areacell_in_file, realm = find_realm(var0)\\n if realm == \\'Amon\\':\\n realm2 = \\'atmos\\'\\n elif realm == \\'Omon\\':\\n realm2 = \\'ocean\\'\\n else:\\n realm2 = realm\\n print(\\'var, areacell_in_file, realm:\\', var, areacell_in_file, realm)\\n #\\n # finding file for \\'mod\\', \\'var\\'\\n #\\n file_name = get_file(modpath(mip=mip, realm=realm, exp=exp, model=mod, realization=run, variable=var0))\\n file_areacell = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=areacell_in_file))\\n if not os.path.isfile(file_areacell):\\n file_areacell = None\\n file_landmask = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=dict_var[\\'landmask\\'][\\'var_name\\']))\\n # -- TEMPORARY --\\n if mip == \\'cmip6\\':\\n if mod in [\\'IPSL-CM6A-LR\\', \\'CNRM-CM6-1\\']:\\n file_landmask = \\'/work/lee1043/ESGF/CMIP6/CMIP/\\'+mod+\\'/sftlf_fx_\\'+mod+\\'_historical_r1i1p1f1_gr.nc\\'\\n elif mod in [\\'GFDL-ESM4\\']:\\n file_landmask = modpath_lf(mip=mip, realm=\"atmos\", model=\\'GFDL-CM4\\', variable=dict_var[\\'landmask\\'][\\'var_name\\'])\\n if mip == \\'cmip5\\':\\n if mod == \"BNU-ESM\":\\n # Incorrect latitude in original sftlf fixed\\n file_landmask = \"/work/lee1043/ESGF/CMIP5/BNU-ESM/sftlf_fx_BNU-ESM_historical_r0i0p0.nc\"\\n elif mod == \"HadCM3\":\\n # Inconsistent lat/lon between sftlf and other variables\\n file_landmask = None \\n # Inconsistent grid between areacella and tauu (probably staggering grid system)\\n file_areacell = None\\n # -- TEMPORARY END --\\n \"\"\"\\n try:\\n areacell_in_file = dict_var[\\'areacell\\'][\\'var_name\\']\\n except:\\n areacell_in_file = None\\n \"\"\"\\n try:\\n landmask_in_file = dict_var[\\'landmask\\'][\\'var_name\\']\\n except:\\n landmask_in_file = None\\n \\n if isinstance(var_in_file, list):\\n list_areacell, list_files, list_landmask, list_name_area, list_name_land = \\\\\\n list(), list(), list(), list(), list()\\n for var1 in var_in_file:\\n areacell_in_file, realm = find_realm(var1)\\n modpath_tmp = get_file(modpath(mip=mip, exp=exp, realm=realm, model=mod, realization=realization, variable=var1))\\n #modpath_lf_tmp = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=dict_var[\\'landmask\\'][\\'var_name\\']))\\n if not os.path.isfile(modpath_tmp):\\n modpath_tmp = None\\n #if not os.path.isfile(modpath_lf_tmp):\\n # modpath_lf_tmp = None\\n file_areacell_tmp = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=areacell_in_file))\\n print(\"file_areacell_tmp:\", file_areacell_tmp)\\n if not os.path.isfile(file_areacell_tmp):\\n file_areacell_tmp = None\\n list_files.append(modpath_tmp)\\n list_areacell.append(file_areacell_tmp)\\n list_name_area.append(areacell_in_file)\\n #list_landmask.append(modpath_lf_tmp)\\n list_landmask.append(file_landmask)\\n list_name_land.append(landmask_in_file)\\n else:\\n if not os.path.isfile(file_name):\\n file_name = None\\n if file_landmask is not None:\\n if not os.path.isfile(file_landmask):\\n file_landmask = None\\n list_files = file_name\\n list_areacell = file_areacell\\n list_name_area = areacell_in_file\\n list_landmask = file_landmask\\n list_name_land = landmask_in_file\\n\\n # Variable from ocean grid\\n if var in [\\'ssh\\']:\\n list_landmask = None\\n # Temporay control of areacello for models with zos on gr instead on gn\\n if mod in [\\'BCC-ESM1\\', \\'CESM2\\', \\'CESM2-FV2\\', \\'CESM2-WACCM\\', \\'CESM2-WACCM-FV2\\',\\n \\'GFDL-CM4\\', \\'GFDL-ESM4\\', \\'MRI-ESM2-0\\', # cmip6\\n #\\'BCC-CSM1-1\\', \\'BCC-CSM1-1-M\\', \\'EC-EARTH\\', \\'GFDL-CM3\\', \\'GISS-E2-R\\',\\n \\'BCC-CSM1-1\\', \\'BCC-CSM1-1-M\\', \\'GFDL-CM3\\', \\'GISS-E2-R\\',\\n \\'MRI-CGCM3\\']: # cmip5\\n list_areacell = None\\n\\n dict_mod[mod_run][var] = {\\n \\'path + filename\\': list_files, \\'varname\\': var_in_file,\\n \\'path + filename_area\\': list_areacell, \\'areaname\\': list_name_area,\\n \\'path + filename_landmask\\': list_landmask, \\'landmaskname\\': list_name_land}\\n\\n print(\\'PMPdriver: var loop end\\')\\n \\n # dictionary needed by EnsoMetrics.ComputeMetricsLib.ComputeCollection\\n dictDatasets = {\\'model\\': dict_mod, \\'observations\\': dict_obs}\\n print(\\'dictDatasets:\\')\\n print(json.dumps(dictDatasets, indent=4, sort_keys=True))\\n\\n # regridding dictionary (only if you want to specify the regridding)\\n dict_regrid = {}\\n \"\"\"\\n # Usage of dict_regrid (select option as below):\\n dict_regrid = {\\n \\'regridding\\': {\\n \\'model_orand_obs\\': 2, \\'regridder\\': \\'cdms\\', \\'regridTool\\': \\'esmf\\', \\'regridMethod\\': \\'linear\\',\\n \\'newgrid_name\\': \\'generic 1x1deg\\'},\\n }\\n \"\"\"\\n\\n # Prepare netcdf file setup\\n json_name = json_name_template(mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id, model=mod, realization=run)\\n netcdf_name = netcdf_name_template(mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id, model=mod, realization=run)\\n netcdf = os.path.join(netcdf_path, netcdf_name)\\n\\n if debug:\\n print(\\'file_name:\\', file_name)\\n print(\\'list_files:\\', list_files)\\n print(\\'netcdf_name:\\', netcdf_name)\\n print(\\'json_name:\\', json_name)\\n\\n # Computes the metric collection\\n print(\"\\\\n### Compute the metric collection ###\\\\n\")\\n cdms2.setAutoBounds(\\'on\\')\\n dict_metric[mod][run], dict_dive[mod][run] = ComputeCollection(mc_name, dictDatasets, mod_run, netcdf=param.nc_out,\\n netcdf_name=netcdf, debug=debug)\\n if debug:\\n print(\\'file_name:\\', file_name)\\n print(\\'list_files:\\', list_files)\\n print(\\'netcdf_name:\\', netcdf_name)\\n print(\\'dict_metric:\\')\\n print(json.dumps(dict_metric, indent=4, sort_keys=True))\\n\\n # OUTPUT METRICS TO JSON FILE (per simulation)\\n metrics_to_json(mc_name, dict_obs, dict_metric, dict_dive, egg_pth, outdir, json_name, mod=mod, run=run)\\n\\n except Exception as e: \\n print(\\'failed for \\', mod, run)\\n print(e)\\n if not debug:\\n pass\\n\\nprint(\\'PMPdriver: model loop end\\')\\n\\n# =================================================\\n# OUTPUT METRICS TO JSON FILE (for all simulations)\\n# -------------------------------------------------\\n#json_name = json_name_template(mip=mip, exp=exp, metricsCollection=mc_name, model=\\'all\\', realization=\\'all\\')\\n#metrics_to_json(mc_name, dict_obs, dict_metric, dict_dive, egg_pth, outdir, json_name)\\n',\n", + " 'userId': 'lee1043'}}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ACCESS1-0',\n", + " 'ACCESS1-3',\n", + " 'BCC-CSM1-1',\n", + " 'BCC-CSM1-1-M',\n", + " 'BNU-ESM',\n", + " 'CCSM4',\n", + " 'CESM1-BGC',\n", + " 'CESM1-CAM5',\n", + " 'CESM1-FASTCHEM',\n", + " 'CESM1-WACCM',\n", + " 'CMCC-CESM',\n", + " 'CMCC-CM',\n", + " 'CMCC-CMS',\n", + " 'CNRM-CM5',\n", + " 'CNRM-CM5-2',\n", + " 'CSIRO-Mk3-6-0',\n", + " 'CSIRO-Mk3L-1-2',\n", + " 'CanCM4',\n", + " 'CanESM2',\n", + " 'EC-EARTH',\n", + " 'FGOALS-g2',\n", + " 'FGOALS-s2',\n", + " 'FIO-ESM',\n", + " 'GFDL-CM2p1',\n", + " 'GFDL-CM3',\n", + " 'GFDL-ESM2G',\n", + " 'GFDL-ESM2M',\n", + " 'GISS-E2-H',\n", + " 'GISS-E2-H-CC',\n", + " 'GISS-E2-R',\n", + " 'GISS-E2-R-CC',\n", + " 'HadCM3',\n", + " 'HadGEM2-AO',\n", + " 'HadGEM2-CC',\n", + " 'HadGEM2-ES',\n", + " 'INMCM4',\n", + " 'IPSL-CM5A-LR',\n", + " 'IPSL-CM5A-MR',\n", + " 'IPSL-CM5B-LR',\n", + " 'MIROC-ESM',\n", + " 'MIROC-ESM-CHEM',\n", + " 'MIROC4h',\n", + " 'MIROC5',\n", + " 'MPI-ESM-LR',\n", + " 'MPI-ESM-MR',\n", + " 'MPI-ESM-P',\n", + " 'MRI-CGCM3',\n", + " 'MRI-ESM1',\n", + " 'NorESM1-M',\n", + " 'NorESM1-ME']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models = list(json_data[\"RESULTS\"][\"model\"].keys())\n", + "models" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pmp_devel_20241202", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e1fd3ab169bf5529ea12e0e0c4328dc168dabe48 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Thu, 6 Feb 2025 13:55:50 -0800 Subject: [PATCH 13/13] add demo notebook --- docs/demo-notebooks.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/demo-notebooks.rst b/docs/demo-notebooks.rst index 5376256f1..830324171 100644 --- a/docs/demo-notebooks.rst +++ b/docs/demo-notebooks.rst @@ -69,9 +69,10 @@ Practical Use Cases examples/return_value_portrait_plot_demo -Data analysis support -~~~~~~~~~~~~~~~~~~~~~ +Data analysis and support +~~~~~~~~~~~~~~~~~~~~~~~~~ .. nbgallery:: - examples/landmask \ No newline at end of file + examples/landmask + examples/pmp_metrics_database \ No newline at end of file